json assignment format

JSON for Beginners – JavaScript Object Notation Explained in Plain English

Many software applications need to exchange data between a client and server.

For a long time, XML was the preferred data format when it came to information exchange between the two points. Then in early 2000, JSON was introduced as an alternate data format for information exchange.

In this article, you will learn all about JSON. You'll understand what it is, how to use it, and we'll clarify a few misconceptions. So, without any further delay, let's get to know JSON.

What is JSON?

JSON ( J ava S cript O bject N otation) is a text-based data exchange format. It is a collection of key-value pairs where the key must be a string type, and the value can be of any of the following types:

A couple of important rules to note:

• In the JSON data format, the keys must be enclosed in double quotes.
• The key and value must be separated by a colon (:) symbol.
• There can be multiple key-value pairs. Two key-value pairs must be separated by a comma (,) symbol.
• No comments (// or /* */) are allowed in JSON data. (But you can get around that , if you're curious.)

Here is how some simple JSON data looks:

Valid JSON data can be in two different formats:

• A collection of key-value pairs enclosed by a pair of curly braces {...} . You saw this as an example above.
• A collection of an ordered list of key-value pairs separated by comma (,) and enclosed by a pair of square brackets [...] . See the example below:

Suppose you are coming from a JavaScript developer background. In that case, you may feel like the JSON format and JavaScript objects (and array of objects) are very similar. But they are not. We will see the differences in detail soon.

The structure of the JSON format was derived from the JavaScript object syntax. That's the only relationship between the JSON data format and JavaScript objects.

JSON is a programming language-independent format. We can use the JSON data format in Python, Java, PHP, and many other programming languages.

JSON Data Format Examples

You can save JSON data in a file with the extension of .json . Let's create an employee.json file with attributes (represented by keys and values) of an employee.

The above JSON data shows the attributes of an employee. The attributes are:

• name : the name of the employee. The value is of String type. So, it is enclosed with double quotes.
• id : a unique identifier of an employee. It is a String type again.
• role : the roles an employee plays in the organization. There could be multiple roles played by an employee. So Array is the preferred data type.
• age : the current age of the employee. It is a Number .
• doj : the date the employee joined the company. As it is a date, it must be enclosed within double-quotes and treated like a String .
• married : is the employee married? If so, true or false. So the value is of Boolean type.
• address : the address of the employee. An address can have multiple parts like street, city, country, zip, and many more. So, we can treat the address value as an Object representation (with key-value pairs).
• referred-by : the id of an employee who referred this employee in the organization. If this employee joined using a referral, this attribute would have value. Otherwise, it will have null as a value.

Now let's create a collection of employees as JSON data. To do that, we need to keep multiple employee records inside the square brackets [...].

Did you notice the referred-by attribute value for the second employee, Bob Washington? It is null . It means he was not referred by any of the employees.

How to Use JSON Data as a String Value

We have seen how to format JSON data inside a JSON file. Alternatively, we can use JSON data as a string value and assign it to a variable. As JSON is a text-based format, it is possible to handle as a string in most programming languages.

Let's take an example to understand how we can do it in JavaScript. You can enclose the entire JSON data as a string within a single quote '...' .

If you want to keep the JSON formatting intact, you can create the JSON data with the help of template literals.

It is also useful when you want to build JSON data using dynamic values.

JavaScript Objects and JSON are NOT the Same

The JSON data format is derived from the JavaScript object structure. But the similarity ends there.

Objects in JavaScript:

• Can have methods, and JSON can't.
• The keys can be without quotes.
• Comments are allowed.
• Are JavaScript's own entity.

Here's a Twitter thread that explains the differences with a few examples.

JavaScript Object and JSON(JavaScript Object Notation) are NOT the same. We often think they are similar. That's NOT TRUE 👀 Let's Understand 🔥 A Thread 🧵 👇 — Tapas Adhikary (@tapasadhikary) November 24, 2021

How to Convert JSON to a JavaScript Object, and vice-versa

JavaScript has two built-in methods to convert JSON data into a JavaScript object and vice-versa.

How to Convert JSON Data to a JavaScript Object

To convert JSON data into a JavaScript object, use the JSON.parse() method. It parses a valid JSON string into a JavaScript object.

How to Convert a JavaScript Object to JSON Data

To convert a JavaScript object into JSON data, use the JSON.stringify() method.

Did you notice the JSON term we used to invoke the parse() and stringify() methods above? That's a built-in JavaScript object named JSON (could have been named JSONUtil as well) but it's not related to the JSON data format we've discussed so far. So, please don't get confused.

How to Handle JSON Errors like "Unexpected token u in JSON at position 1"?

While handling JSON, it is very normal to get an error like this while parsing the JSON data into a JavaScript object:

Whenever you encounter this error, please question the validity of your JSON data format. You probably made a trivial error and that is causing it. You can validate the format of your JSON data using a JSON Linter .

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A Beginner's Guide to JSON with Examples

Syntax and data types, json strings, json numbers, json booleans, json objects, json arrays, nesting objects & arrays, transforming json data in javascript, json vs xml, json resources, further reading.

JSON — short for JavaScript Object Notation — is a popular format for storing and exchanging data. As the name suggests, JSON is derived from JavaScript but later embraced by other programming languages.

JSON file ends with a .json extension but not compulsory to store the JSON data in a file. You can define a JSON object or an array in JavaScript or HTML files.

In a nutshell, JSON is lightweight, human-readable, and needs less formatting, which makes it a good alternative to XML.

JSON data is stored as key-value pairs similar to JavaScript object properties, separated by commas, curly braces, and square brackets. A key-value pair consists of a key , called name (in double quotes), followed by a colon ( : ), followed by value (in double-quotes):

Multiple key-value pairs are separated by a comma:

JSON keys are strings , always on the left of the colon, and must be wrapped in double quotes . Within each object, keys need to be unique and can contain whitespaces , as in "author name": "John Doe" .

It is not recommended to use whitespaces in keys. It will make it difficult to access the key during programming. Instead, use an underscore in keys as in "author_name": "John Doe" .

JSON values must be one of the following data types:

• Boolean ( true or false )

Note: Unlike JavaScript, JSON values cannot be a function, a date or undefined .

String values in JSON are a set of characters wrapped in double-quotes:

A number value in JSON must be an integer or a floating-point:

Boolean values are simple true or false in JSON:

Null values in JSON are empty words:

JSON objects are wrapped in curly braces. Inside the object, we can list any number of key-value pairs, separated by commas:

JSON arrays are wrapped in square brackets. Inside an array, we can declare any number of objects, separated by commas:

In the above JSON array, there are three objects. Each object is a record of a person (with name, gender, and age).

JSON can store nested objects and arrays as values assigned to keys. It is very helpful for storing different sets of data in one file:

The JSON format is syntactically similar to the way we create JavaScript objects. Therefore, it is easier to convert JSON data into JavaScript native objects.

JavaScript built-in JSON object provides two important methods for encoding and decoding JSON data: parse() and stringify() .

JSON.parse() takes a JSON string as input and converts it into JavaScript object:

JSON.stringify() does the opposite. It takes a JavaScript object as input and transforms it into a string that represents it in JSON:

A few years back, XML (Extensible Markup Language) was a popular choice for storing and sharing data over the network. But that is not the case anymore.

JSON has emerged as a popular alternative to XML for the following reasons:

• Less verbose — XML uses many more words than required, which makes it time-consuming to read and write.
• Lightweight & faster — XML must be parsed by an XML parser, but JSON can be parsed using JavaScript built-in functions. Parsing large XML files is slow and requires a lot of memory.
• More data types — You cannot store arrays in XML which are extensively used in JSON format.

Let us see an example of an XML document and then the corresponding document written in JSON:

databases.xml

databases.json

As you can see above, the XML structure is not intuitive , making it hard to represent in code. On the other hand, the JSON structure is much more compact and intuitive , making it easy to read and map directly to domain objects in any programming language.

There are many useful resources available online for free to learn and work with JSON:

• Introducing JSON — Learn the JSON language supported features.
• JSONLint — A JSON validator that you can use to verify if the JSON string is valid.
• JSON.dev — A little tool for viewing, parsing, validating, minifying, and formatting JSON.
• JSON Schema — Annotate and validate JSON documents according to your own specific format.

A few more articles related to JSON that you might be interested in:

• How to read and write JSON files in Node.js
• Reading and Writing JSON Files in Java
• How to read and write JSON using Jackson in Java
• How to read and write JSON using JSON.simple in Java
• Understanding JSON.parse() and JSON.stringify()
• Processing JSON Data in Spring Boot
• Export PostgreSQL Table Data as JSON

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You might also like...

• How to convert XML to JSON in Node.js
• How to send JSON request using XMLHttpRequest (XHR)
• How to read JSON from a file using Gson in Java
• How to write JSON to a file using Gson in Java
• Read and write JSON as a stream using Gson
• How to pretty print JSON using Gson in Java

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Working with JSON

• Overview: Introducing JavaScript objects

JavaScript Object Notation (JSON) is a standard text-based format for representing structured data based on JavaScript object syntax. It is commonly used for transmitting data in web applications (e.g., sending some data from the server to the client, so it can be displayed on a web page, or vice versa). You'll come across it quite often, so in this article, we give you all you need to work with JSON using JavaScript, including parsing JSON so you can access data within it, and creating JSON.

No, really, what is JSON?

JSON is a text-based data format following JavaScript object syntax, which was popularized by Douglas Crockford . Even though it closely resembles JavaScript object literal syntax, it can be used independently from JavaScript, and many programming environments feature the ability to read (parse) and generate JSON.

JSON exists as a string — useful when you want to transmit data across a network. It needs to be converted to a native JavaScript object when you want to access the data. This is not a big issue — JavaScript provides a global JSON object that has methods available for converting between the two.

Note: Converting a string to a native object is called deserialization , while converting a native object to a string so it can be transmitted across the network is called serialization .

A JSON string can be stored in its own file, which is basically just a text file with an extension of .json , and a MIME type of application/json .

JSON structure

As described above, JSON is a string whose format very much resembles JavaScript object literal format. You can include the same basic data types inside JSON as you can in a standard JavaScript object — strings, numbers, arrays, booleans, and other object literals. This allows you to construct a data hierarchy, like so:

If we loaded this string into a JavaScript program and parsed it into a variable called superHeroes for example, we could then access the data inside it using the same dot/bracket notation we looked at in the JavaScript object basics article. For example:

To access data further down the hierarchy, you have to chain the required property names and array indexes together. For example, to access the third superpower of the second hero listed in the members list, you'd do this:

• First, we have the variable name — superHeroes .
• Inside that, we want to access the members property, so we use ["members"] .
• members contains an array populated by objects. We want to access the second object inside the array, so we use [1] .
• Inside this object, we want to access the powers property, so we use ["powers"] .
• Inside the powers property is an array containing the selected hero's superpowers. We want the third one, so we use [2] .

Note: We've made the JSON seen above available inside a variable in our JSONTest.html example (see the source code ). Try loading this up and then accessing data inside the variable via your browser's JavaScript console.

Arrays as JSON

Above we mentioned that JSON text basically looks like a JavaScript object inside a string. We can also convert arrays to/from JSON. Below is also valid JSON, for example:

The above is perfectly valid JSON. You'd just have to access array items (in its parsed version) by starting with an array index, for example [0]["powers"][0] .

Other notes

• JSON is purely a string with a specified data format — it contains only properties, no methods.
• JSON requires double quotes to be used around strings and property names. Single quotes are not valid other than surrounding the entire JSON string.
• Even a single misplaced comma or colon can cause a JSON file to go wrong, and not work. You should be careful to validate any data you are attempting to use (although computer-generated JSON is less likely to include errors, as long as the generator program is working correctly). You can validate JSON using an application like JSONLint .
• JSON can actually take the form of any data type that is valid for inclusion inside JSON, not just arrays or objects. So for example, a single string or number would be valid JSON.
• Unlike in JavaScript code in which object properties may be unquoted, in JSON only quoted strings may be used as properties.

Active learning: Working through a JSON example

So, let's work through an example to show how we could make use of some JSON formatted data on a website.

Getting started

To begin with, make local copies of our heroes.html and style.css files. The latter contains some simple CSS to style our page, while the former contains some very simple body HTML, plus a <script> element to contain the JavaScript code we will be writing in this exercise:

We have made our JSON data available on our GitHub, at https://mdn.github.io/learning-area/javascript/oojs/json/superheroes.json .

We are going to load the JSON into our script, and use some nifty DOM manipulation to display it, like this:

Top-level function

The top-level function looks like this:

To obtain the JSON, we use an API called Fetch . This API allows us to make network requests to retrieve resources from a server via JavaScript (e.g. images, text, JSON, even HTML snippets), meaning that we can update small sections of content without having to reload the entire page.

In our function, the first four lines use the Fetch API to fetch the JSON from the server:

• we declare the requestURL variable to store the GitHub URL
• we use the URL to initialize a new Request object.
• we make the network request using the fetch() function, and this returns a Response object
• we retrieve the response as JSON using the json() function of the Response object.

Note: The fetch() API is asynchronous . We'll learn a lot about asynchronous functions in the next module , but for now, we'll just say that we need to add the keyword async before the name of the function that uses the fetch API, and add the keyword await before the calls to any asynchronous functions.

After all that, the superHeroes variable will contain the JavaScript object based on the JSON. We are then passing that object to two function calls — the first one fills the <header> with the correct data, while the second one creates an information card for each hero on the team, and inserts it into the <section> .

Populating the header

Now that we've retrieved the JSON data and converted it into a JavaScript object, let's make use of it by writing the two functions we referenced above. First of all, add the following function definition below the previous code:

Here we first create an h1 element with createElement() , set its textContent to equal the squadName property of the object, then append it to the header using appendChild() . We then do a very similar operation with a paragraph: create it, set its text content and append it to the header. The only difference is that its text is set to a template literal containing both the homeTown and formed properties of the object.

Creating the hero information cards

Next, add the following function at the bottom of the code, which creates and displays the superhero cards:

To start with, we store the members property of the JavaScript object in a new variable. This array contains multiple objects that contain the information for each hero.

Next, we use a for...of loop to loop through each object in the array. For each one, we:

• Create several new elements: an <article> , an <h2> , three <p> s, and a <ul> .
• Set the <h2> to contain the current hero's name .
• Fill the three paragraphs with their secretIdentity , age , and a line saying "Superpowers:" to introduce the information in the list.
• Store the powers property in another new constant called superPowers — this contains an array that lists the current hero's superpowers.
• Use another for...of loop to loop through the current hero's superpowers — for each one we create an <li> element, put the superpower inside it, then put the listItem inside the <ul> element ( myList ) using appendChild() .
• The very last thing we do is to append the <h2> , <p> s, and <ul> inside the <article> ( myArticle ), then append the <article> inside the <section> . The order in which things are appended is important, as this is the order they will be displayed inside the HTML.

Note: If you are having trouble getting the example to work, try referring to our heroes-finished.html source code (see it running live also.)

Note: If you are having trouble following the dot/bracket notation we are using to access the JavaScript object, it can help to have the superheroes.json file open in another tab or your text editor, and refer to it as you look at our JavaScript. You should also refer back to our JavaScript object basics article for more information on dot and bracket notation.

Calling the top-level function

Finally, we need to call our top-level populate() function:

Converting between objects and text

The above example was simple in terms of accessing the JavaScript object, because we converted the network response directly into a JavaScript object using response.json() .

But sometimes we aren't so lucky — sometimes we receive a raw JSON string, and we need to convert it to an object ourselves. And when we want to send a JavaScript object across the network, we need to convert it to JSON (a string) before sending it. Luckily, these two problems are so common in web development that a built-in JSON object is available in browsers, which contains the following two methods:

• parse() : Accepts a JSON string as a parameter, and returns the corresponding JavaScript object.
• stringify() : Accepts an object as a parameter, and returns the equivalent JSON string.

You can see the first one in action in our heroes-finished-json-parse.html example (see the source code ) — this does exactly the same thing as the example we built up earlier, except that:

• we retrieve the response as text rather than JSON, by calling the text() method of the response
• we then use parse() to convert the text to a JavaScript object.

The key snippet of code is here:

As you might guess, stringify() works the opposite way. Try entering the following lines into your browser's JavaScript console one by one to see it in action:

Here we're creating a JavaScript object, then checking what it contains, then converting it to a JSON string using stringify() — saving the return value in a new variable — then checking it again.

Test your skills!

You've reached the end of this article, but can you remember the most important information? You can find some further tests to verify that you've retained this information before you move on — see Test your skills: JSON .

In this article, we've given you a simple guide to using JSON in your programs, including how to create and parse JSON, and how to access data locked inside it. In the next article, we'll begin looking at object-oriented JavaScript.

• JSON reference
• Fetch API overview
• Using Fetch
• HTTP request methods
• Official JSON website with link to ECMA standard

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JSON Tutorial – Introduction, Structure, Syntax Rules, and Data Exchange

In this tutorial, we’ll introduce the JSON data exchange format . This post covers a JSON object’s structure, JSON syntax rules, data exchange with JSON, and programming languages support for JSON.

What is JSON?

• JSON is a lightweight, human-readable data-interchange format.
• JSON is used to store a collection of name-value pairs or an ordered list of values.
• JSON is useful for serializing objects , and arrays for transmitting over the network.
• JSON is very easy to parse and generate and doesn’t use a full markup structure like an XML.
• JSON became a popular alternative of XML format for its fast asynchronous client–server communication.
• All JSON files have the extension .json .

Structure of a JSON Object:

A JSON can be:

JSON Example:

  In the above example,

• The first two name-value pairs maps a string to another string.
• The third name-value pair maps a string age with a number 25.
• The fourth name-value pair maps a string children with an empty array [] .
• The fifth name-value pair maps a string spouse with a null value.
• The sixth name-value pair maps a string address with another JSON object.
• The seventh name-value pair maps a string phoneNumbers with an array of JSON objects.

JSON Syntax Rules:

• A JSON object is surrounded by curly braces {} .
• The name-value pairs are grouped by a colon (:) and separated by a comma (,) .
• An array begins with a left bracket and ends with a right bracket [] .
• The trailing commas and leading zeros in a number are prohibited.
• The octal and hexadecimal formats are not permitted.
• Each key within the JSON should be unique and should be enclosed within the double-quotes.
• The boolean type matches only two special values: true and false and NULL values are represented by the null literal (without quotes).

JavaScript JSON built-in library:

JavaScript JSON built-in library provides two functions to decode and encode JSON objects – JSON.parse() and JSON.stringify() .

  1. JSON.stringify() returns a JSON string corresponding to a JavaScript object.

  Output: {“fruit”:”Apple”, “types”:[“Small”,”Medium”,”Large”], “quantity”:1000}

  2. JSON.parse() is a safe and fast method of decoding a JSON string as JavaScript object.

  Output: Apple, [Small,Medium,Large], 1000

  We can also parse a JSON string into a JavaScript object by invoking the eval() function on the JSON string wrapped by parenthesis. This works since JSON is derived from JavaScript. eval() is an evil function, which should be avoided at all costs. This is because eval can execute any malicious scripts on the user’s machine with the caller’s privileges. Moreover, malicious code can find the scope in which eval() was invoked, making the website vulnerable to attacks. JSON.parse() is the safe and fast alternative of eval, which safely fails on malicious code. JSON is included in almost all modern browsers. For old versions of browsers, use an external JavaScript library such as Douglas Crockford’s json2.js .

Data Exchange with JSON:

JSON is primarily used for the transmission of serialized text between a browser and a server.

The official Media type for JSON is application/json .

Programming Languages Support:

Originally, JSON was intended to be a subset of the JavaScript languages, but now almost all major programming languages support JSON due to its language-independent data format. JSON’s official website lists major JSON libraries available in various programming languages which can be used to parse and generate JSON. For instance, the most popular JSON libraries for Java are GSON, JSON.simple, Jackson, and JSONP.

That’s all about JSON data exchange format.

  Useful links:

• JSON Validator
• JSON Formatter
• JSON Minifier
• JSON Editor
• JSON to XML
• JSON to YAML
• JSON to CSV

XML Tutorial – Introduction, Structure, and Syntax Rules

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A beginner's guide to JSON, the data format for the internet

When APIs send data, chances are they send it as JSON objects. Here's a primer on why JSON is how networked applications send data.

As the web grows in popularity and power, so does the amount of data stored and transferred between systems, many of which know nothing about each other. From early on, the format that this data was transferred in mattered, and like the web, the best formats were open standards that anyone could use and contribute to. XML gained early popularity, as it looked like HTML, the foundation of the web. But it was clunky and confusing.

That’s where JSON (JavaScript Object Notation) comes in. If you’ve consumed an API in the last five to ten years, you’ve probably seen JSON data. While the format was first developed in the early 2000s, the first standards were published in 2006. Understanding what JSON is and how it works is a foundational skill for any web developer.

In this article, we’ll cover the basics of what JSON looks like and how to use it in your web applications, as well as talk about serialized JSON—JST and JWT—and the competing data formats.

What JSON looks like

JSON is a human-readable format for storing and transmitting data. As the name implies, it was originally developed for JavaScript, but can be used in any language and is very popular in web applications. The basic structure is built from one or more keys and values:

You’ll often see a collection of key:value pairs enclosed in brackets described as a JSON object. While the key is any string, the value can be a string, number, array, additional object, or the literals, false, true and null. For example, the following is valid JSON:

JSON doesn't have to have only key:value pairs; the specification allows to any value to be passed without a key. However, almost all of the JSON objects that you see will contain key:value pairs.

Using JSON in API calls

One of the most common uses for JSON is when using an API, both in requests and responses. It is much more compact than other standards and allows for easy consumption in web browsers as JavaScript can easily parse JSON strings, only requiring JSON.parse() to start using it.

JSON.parse(string) takes a string of valid JSON and returns a JavaScript object. For example, it can be called on the body of an API response to give you a usable object. The inverse of this function is JSON.stringify(object) which takes a JavaScript object and returns a string of JSON, which can then be transmitted in an API request or response.

JSON isn’t required by REST or GraphQL, both very popular API formats. However, they are often used together, particularly with GraphQL, where it is best practice to use JSON due to it being small and mostly text. If necessary, it compresses very well with GZIP.

GraphQL's requests aren’t made in JSON, instead using a system that resembles JSON, like this

Which will return the relevant data, and if using JSON, it will match very closely:

Using JSON files in JavaScript

In some cases, you may want to load JSON from a file, such as for configuration files or mock data. Using pure JavaScript, it currently isn’t possible to import a JSON file, however a proposal has been created to allow this . In addition, it is a very common feature in bundlers and compilers, like webpack and Babel . Currently, you can get equivalent functionality by exporting a JavaScript Object the same as your desired JSON from a JavaScript file.

export const data = {"foo": "bar"}

Now this object will be stored in the constant, data, and will be accessible throughout your application using import or require statements. Note that this will import a copy of the data, so modifying the object won’t write the data back to the file or allow the modified data to be used in other files.

Accessing and modifying JavaScript objects

Once you have a variable containing your data, in this example data, to access a key’s value inside it, you could use either data.key or data["key"]. Square brackets must be used for array indexing; for example if that value was an array, you could do data.key[0], but data.key.0 wouldn’t work.

Object modification works in the same way. You can just set data.key = "foo" and that key will now have the value “foo”. Although only the final element in the chain of objects can be replaced; for example if you tried to set data.key.foo.bar to something, it would fail as you would first have to set data.key.foo to an object.

Comparison to YAML and XML

JSON isn’t the only web-friendly data standard out there. The major competitor for JSON in APIs is XML. Instead of the following JSON:

in XML, you’d instead have:

JSON was standardized much later than XML, with the specification for XML coming in 1998, whereas Ecma International standardized JSON in 2013. XML was extremely popular and seen in standards such as AJAX (Asynchronous JavaScript and XML) and the XMLHttpRequest function in JavaScript.

XML used by a major API standard: Simple Object Access Protocol (SOAP). This standard can be significantly more verbose than REST and GraphQL, in part due to the usage of XML and because the standard includes more information, such as describing the XML namespace as part of the envelope system. This might be a reason why SOAP usage has declined for years.

Another alternative is YAML, which is much more similar in length to JSON compared to XML, with the same example being:

However, unlike XML, YAML doesn’t really compete with JSON as an API data format. Instead, it’s primarily used for configuration files— Kubernetes primarily uses YAML to configure infrastructure. YAML offers features that JSON doesn’t have, such as comments. Unlike JSON and XML, browsers cannot parse YAML, so a parser would need to be added as a library if you want to use YAML for data interchange.

JSON methods, toJSON

Let’s say we have a complex object, and we’d like to convert it into a string, to send it over a network, or just to output it for logging purposes.

Naturally, such a string should include all important properties.

We could implement the conversion like this:

…But in the process of development, new properties are added, old properties are renamed and removed. Updating such toString every time can become a pain. We could try to loop over properties in it, but what if the object is complex and has nested objects in properties? We’d need to implement their conversion as well.

Luckily, there’s no need to write the code to handle all this. The task has been solved already.

JSON.stringify

The JSON (JavaScript Object Notation) is a general format to represent values and objects. It is described as in RFC 4627 standard. Initially it was made for JavaScript, but many other languages have libraries to handle it as well. So it’s easy to use JSON for data exchange when the client uses JavaScript and the server is written on Ruby/PHP/Java/Whatever.

JavaScript provides methods:

• JSON.stringify to convert objects into JSON.
• JSON.parse to convert JSON back into an object.

For instance, here we JSON.stringify a student:

The method JSON.stringify(student) takes the object and converts it into a string.

The resulting json string is called a JSON-encoded or serialized or stringified or marshalled object. We are ready to send it over the wire or put into a plain data store.

Please note that a JSON-encoded object has several important differences from the object literal:

• Strings use double quotes. No single quotes or backticks in JSON. So 'John' becomes "John" .
• Object property names are double-quoted also. That’s obligatory. So age:30 becomes "age":30 .

JSON.stringify can be applied to primitives as well.

JSON supports following data types:

• Objects { ... }
• Arrays [ ... ]
• boolean values true/false ,

For instance:

JSON is data-only language-independent specification, so some JavaScript-specific object properties are skipped by JSON.stringify .

• Function properties (methods).
• Symbolic keys and values.
• Properties that store undefined .

Usually that’s fine. If that’s not what we want, then soon we’ll see how to customize the process.

The great thing is that nested objects are supported and converted automatically.

The important limitation: there must be no circular references.

Here, the conversion fails, because of circular reference: room.occupiedBy references meetup , and meetup.place references room :

Excluding and transforming: replacer

The full syntax of JSON.stringify is:

Most of the time, JSON.stringify is used with the first argument only. But if we need to fine-tune the replacement process, like to filter out circular references, we can use the second argument of JSON.stringify .

If we pass an array of properties to it, only these properties will be encoded.

Here we are probably too strict. The property list is applied to the whole object structure. So the objects in participants are empty, because name is not in the list.

Let’s include in the list every property except room.occupiedBy that would cause the circular reference:

Now everything except occupiedBy is serialized. But the list of properties is quite long.

Fortunately, we can use a function instead of an array as the replacer .

The function will be called for every (key, value) pair and should return the “replaced” value, which will be used instead of the original one. Or undefined if the value is to be skipped.

In our case, we can return value “as is” for everything except occupiedBy . To ignore occupiedBy , the code below returns undefined :

Please note that replacer function gets every key/value pair including nested objects and array items. It is applied recursively. The value of this inside replacer is the object that contains the current property.

The first call is special. It is made using a special “wrapper object”: {"": meetup} . In other words, the first (key, value) pair has an empty key, and the value is the target object as a whole. That’s why the first line is ":[object Object]" in the example above.

The idea is to provide as much power for replacer as possible: it has a chance to analyze and replace/skip even the whole object if necessary.

Formatting: space

The third argument of JSON.stringify(value, replacer, space) is the number of spaces to use for pretty formatting.

Previously, all stringified objects had no indents and extra spaces. That’s fine if we want to send an object over a network. The space argument is used exclusively for a nice output.

Here space = 2 tells JavaScript to show nested objects on multiple lines, with indentation of 2 spaces inside an object:

The third argument can also be a string. In this case, the string is used for indentation instead of a number of spaces.

The space parameter is used solely for logging and nice-output purposes.

Custom “toJSON”

Like toString for string conversion, an object may provide method toJSON for to-JSON conversion. JSON.stringify automatically calls it if available.

Here we can see that date (1) became a string. That’s because all dates have a built-in toJSON method which returns such kind of string.

Now let’s add a custom toJSON for our object room (2) :

As we can see, toJSON is used both for the direct call JSON.stringify(room) and when room is nested in another encoded object.

To decode a JSON-string, we need another method named JSON.parse .

The syntax:

Or for nested objects:

The JSON may be as complex as necessary, objects and arrays can include other objects and arrays. But they must obey the same JSON format.

Here are typical mistakes in hand-written JSON (sometimes we have to write it for debugging purposes):

Besides, JSON does not support comments. Adding a comment to JSON makes it invalid.

There’s another format named JSON5 , which allows unquoted keys, comments etc. But this is a standalone library, not in the specification of the language.

The regular JSON is that strict not because its developers are lazy, but to allow easy, reliable and very fast implementations of the parsing algorithm.

Using reviver

Imagine, we got a stringified meetup object from the server.

It looks like this:

…And now we need to deserialize it, to turn back into JavaScript object.

Let’s do it by calling JSON.parse :

Whoops! An error!

The value of meetup.date is a string, not a Date object. How could JSON.parse know that it should transform that string into a Date ?

Let’s pass to JSON.parse the reviving function as the second argument, that returns all values “as is”, but date will become a Date :

By the way, that works for nested objects as well:

• JSON is a data format that has its own independent standard and libraries for most programming languages.
• JSON supports plain objects, arrays, strings, numbers, booleans, and null .
• JavaScript provides methods JSON.stringify to serialize into JSON and JSON.parse to read from JSON.
• Both methods support transformer functions for smart reading/writing.
• If an object has toJSON , then it is called by JSON.stringify .

Turn the object into JSON and back

Turn the user into JSON and then read it back into another variable.

Exclude backreferences

In simple cases of circular references, we can exclude an offending property from serialization by its name.

But sometimes we can’t just use the name, as it may be used both in circular references and normal properties. So we can check the property by its value.

Write replacer function to stringify everything, but remove properties that reference meetup :

Here we also need to test key=="" to exclude the first call where it is normal that value is meetup .

• If you have suggestions what to improve - please submit a GitHub issue or a pull request instead of commenting.
• If you can't understand something in the article – please elaborate.
• To insert few words of code, use the <code> tag, for several lines – wrap them in <pre> tag, for more than 10 lines – use a sandbox ( plnkr , jsbin , codepen …)

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What is JSON? The universal data format

Json is the leading data interchange format for web applications and more. here’s what you need to know about javascript object notation..

Contributor, InfoWorld |

A little bit of history

Why developers use json, how json works, json vs. xml, json vs. yaml and csv, complex json: nesting, objects, and arrays, parsing and generating json, json schema and json formatter, using json with typescript.

JSON, or JavaScript Object Notation, is a format used to represent data. It was introduced in the early 2000s as part of JavaScript and gradually expanded to become the most common medium for describing and exchanging text-based data. Today, JSON is the universal standard of data exchange. It is found in every area of programming, including front-end and server-side development, systems, middleware, and databases.

This article introduces you to JSON. You'll get an overview of the technology, find out how it compares to similar standards like XML, YAML, and CSV, and see examples of JSON in a variety of programs and use cases.

JSON was initially developed as a format for communicating between JavaScript clients and back-end servers. It quickly gained popularity as a human-readable format that front-end programmers could use to communicate with the back end using a terse, standardized format. Developers also discovered that JSON was very flexible: you could add, remove, and update fields ad hoc. (That flexibility came at the cost of safety, which was later addressed with the JSON schema.)

In a curious turn, JSON was popularized by the AJAX revolution . Strange, given the emphasis on XML, but it was JSON that made AJAX really shine. Using REST as the convention for APIs and JSON as the medium for exchange proved a potent combination for balancing simplicity, flexibility, and consistency.

Next, JSON spread from front-end JavaScript to client-server communication, and from there to system config files, back-end languages, and all the way to databases. JSON even helped spur the NoSQL movement that revolutionized data storage. It turned out that database administrators also enjoyed JSON's flexibility and ease of programming.

Today, document-oriented data stores like MongoDB provide an API that works with JSON-like data structures. In an interview in early 2022, MongoDB CTO Mark Porter noted that, from his perspective, JSON is still pushing the frontier of data .  Not bad for a data format that started with a humble curly brace and a colon.

No matter what type of program or use case they're working on, software developers need a way to describe and exchange data. This need is found in databases, business logic, user interfaces, and in all systems communication. There are many approaches to structuring data for exchange. The two broad camps are binary and text-based data. JSON is a text-based format, so it is readable by both people and machines.

JSON is a wildly successful way of formatting data for several reasons. First, it's native to JavaScript, and it's used inside of JavaScript programs as JSON literals. You can also use JSON with other programming languages, so it's useful for data exchange between heterogeneous systems. Finally, it is human readable. For a language data structure, JSON is an incredibly versatile tool. It is also fairly painless to use, especially when compared to other formats. 

When you enter your username and password into a form on a web page, you are interacting with an object with two fields: username and password. As an example, consider the login page in Figure 1.

Figure 1. A simple login page.

Listing 1 shows this page described using JSON.

Listing 1. JSON for a login page

Everything inside of the braces or squiggly brackets ( {...} ) belongs to the same object. An object , in this case, refers in the most general sense to a “single thing." Inside the braces are the properties that belong to the thing. Each property has two parts: a name and a value, separated by a colon. These are known as the keys and values. In Listing 1, "username" is a key and "Bilbo Baggins" is a value.

The key takeaway here is that JSON does everything necessary to handle the need—in this case, holding the information in the form—without a lot of extra information. You can glance at this JSON file and understand it. That is why we say that JSON is concise . Conciseness also makes JSON an excellent format for sending over the wire. 

JSON was created as an alternative to XML, which was once the dominant format for data exchange. The login form in Listing 2 is described using XML.

Listing 2. Login form in XML

Yikes!  Just looking at this form is tiring. Imagine having to create and parse it in code. In contrast, using JSON in JavaScript is dead simple. Try it out. Hit F12 in your browser to open a JavaScript console, then paste in the JSON shown in Listing 3.

Listing 3. Using JSON in JavaScript

XML is hard to read and leaves much to be desired in terms of coding agility. JSON was created to resolve these issues. It's no wonder it has more or less supplanted XML.

Two data formats sometimes compared to JSON are YAML and CSV. The two formats are on opposite ends of the temporal spectrum. CSV is an ancient, pre-digital format that eventually found its way to being used in computers. YAML was inspired by JSON and is something of its conceptual descendant.

CSV is a simple list of values, with each entry denoted by a comma or other separator character, with an optional first row of header fields. It is rather limited as a medium of exchange and programming structure, but it is still useful for outputting large amounts of data to disk. And, of course, CSV's organization of tabular data is perfect for things like spreadsheets.

YAML is actually a superset of JSON, meaning it will support anything JSON supports. But YAML also supports a more stripped-down syntax, intended to be even more concise than JSON. For example, YAML uses indentation for hierarchy, forgoing the braces. Although YML is sometimes used as a data exchange format, its biggest use case is in configuration files.

So far, you've only seen examples of JSON used with shallow (or simple) objects. That just means every field on the object holds the value of a primitive. JSON is also capable of modeling arbitrary complex data structures such as object graphs and cyclic graphs—that is, structures with circular references. In this section, you'll see examples of complex modeling via nesting, object references, and arrays.

JSON with nested objects

Listing 4 shows how to define nested JSON objects.

Listing 4. Nested JSON

The bestfriend property in Listing 4 refers to another object, which is defined inline as a JSON literal.

JSON with object references

Now consider Listing 5, where instead of holding a name in the bestfriend property, we hold a reference to the actual object.

Listing 5. An object reference

In Listing 5, we put the handle to the merry object on the bestfriend property. Then, we are able to obtain the actual merry object off the pippin object via the bestfriend property. We obtained the name off the merry object with the name property. This is called traversing the object graph , which is done using the dot operator.

JSON with arrays

Another type of structure that JSON properties can have is arrays. These look just like JavaScript arrays and are denoted with a square bracket, as shown in Listing 6.

Listing 6. An array property

Of course, arrays may hold references to other objects, as well. With these two structures, JSON can model any range of complex object relations.

Parsing and generating JSON means reading it and creating it, respectively. You’ve seen JSON.stringify() in action already. That is the built-in mechanism for JavaScript programs to take an in-memory object representation and turn it into a JSON string. To go in the other direction—that is, take a JSON string and turn it into an in-memory object—you use JSON.parse() .

In most other languages, it’s necessary to use a third-party library for parsing and generating. For example, in Java there are numerous libraries , but the most popular are Jackson and GSON . These libraries are more complex than stringify and parse in JavaScript, but they also offer advanced capabilities such as mapping to and from custom types and dealing with other data formats.

In JavaScript, it is common to send and receive JSON to servers. For example with the built in fetch() API.  When doing so, you can automatically parse the response, as shown in Listing 7. 

Listing 7. Parsing a JSON response with fetch()

Once you turn JSON into an in-memory data structure, be it JavaScript or another language, you can employ the APIs for manipulating the structure. For example, in JavaScript, the JSON parsed in Listing 7 would be accessed like any other JavaScript object—perhaps by looping through data.keys or accessing known properties on the data object.

JavaScript and JSON are incredibly flexible, but sometimes you need more structure than they provide. In a language like Java, strong typing and abstract types (like interfaces) help structure large-scale programs. In SQL stores, a schema provides a similar structure. If you need more structure in your JSON documents, you can use JSON schema to explicitly define the characteristics of your JSON objects. Once defined, you can use the schema to validate object instances and ensure that they conform to the schema.

Another issue is dealing with machine-processed JSON that is minified and illegible. Fortunately, this problem is easy to solve. Just jump over to the JSON Formatter & Validator (I like this tool but there are others), paste in your JSON, and hit the Process button. You'll see a human-readable version that you can use. Most IDEs also have a built-in JavaScript formatter to format your JSON.

TypeScript allows for defining types and interfaces, so there are times when using JSON with TypeScript is useful. A class, like a schema, outlines the acceptable properties of an instance of a given type. In plain JavaScript there’s no way to restrict properties and their types. JavaScript classes are like suggestions; the programmer can set them now and modify the JSON later. A TypeScript class, however, enforces what properties the JSON can have and what types they can be.

JSON is one of the most essential technologies used in the modern software landscape. It is crucial to JavaScript but also used as a common mode of interaction between a wide range of technologies. Fortunately, the very thing that makes JSON so useful makes it relatively easy to understand. It is a concise and readable format for representing textual data.

Next read this:

• Why companies are leaving the cloud
• 5 easy ways to run an LLM locally
• Coding with AI: Tips and best practices from developers
• Meet Zig: The modern alternative to C
• What is generative AI? Artificial intelligence that creates
• The best open source software of 2023
• Web Development
• Software Development

Matthew Tyson is a founder of Dark Horse Group, Inc. He believes in people-first technology. When not playing guitar, Matt explores the backcountry and the philosophical hinterlands. He has written for JavaWorld and InfoWorld since 2007.

Copyright © 2022 IDG Communications, Inc.

JSON Basics For Beginners-With Examples and Exercises

Having a good working knowledge of JSON, and how to create and use JSON data will be very important in developing IOT applications.

The tutorial is split into two sections. The first section we look at creating JSON data and in the second section we look at converting JSON data into JavaScript objects and extracting values from the data.

In this tutorial and Workbook you will learn:

• What JSON is and Why it is Used.
• Basic JSON format .

Sending and Receiving JSON Data

Creating json formatted data, create json string examples.

• How to convert JSON to JavaScript and vice versa .
• How to create complex JSON strings using the online JSON editor .
• How to extract data from JavaScript objects and arrays .

What is JSON ( JavaScript Object Notation )?

JSON is a format for encoding data in human readable format for storing and sending over a network.

Although it started in JavaScript it is used in all modern programming languages.

Why it is Used?

JSON is used because it makes it easy to store and transfer JavaScript arrays and objects as text data.

JSON Format Overview

JSON stores data as:

• key/value pairs
• Data is separated using commas
• Text data is enclosed in double quotes
• Numerical data has no quotes.
• Arrays are enclosed in square brackets []
• Objects are enclosed in curly brackets {}
• The output is a text string

Data from devices and sensors in IOT applications is normally sent in JSON format .

So the application program on the sensor has to package the data into a JSON string , and the receiving application has to convert the JSON string into the original data format e.g. object, array etc .

All major programming languages have functions for doing this.

You can create a JSON string manually but when coding complex data structures in it common to use a JSON editor .

In addition to verify that the encoding is correct it is also common to use a JSON validator .

We will be looking at examples and doing exercises involving both later in this tutorial.

The exercises consist of a worked example(s) and questions to test your understanding.

Worked Example 1:

Convert the following JavaScript array to JSON.

var names= [“james”, “jake”];

[“james”, “jake”]

Worked Example 2:

Convert the following JavaScript object to JSON

var power={voltage: 250,current: 12}

‘{“voltage”: 250,”current”: 12}’

Note: In JSON numbers do not need quotes

Worked Example 3:

var power={voltage: “250”,current: “12”};

‘{“voltage”: “250”, “current”: “12”}’

Note: This time even though they are numbers they are represented as strings in JavaScript and so they need quotes.

Answers to all questions are at the end.

Q1 -The following JSON string consists of

‘[{“sensor”: “sensor1”, “temperature”: 22, “humidity”: 80}]’

• An Array inside an object
• An object inside and array

Q2 – The JSON string consists of:

‘[{“sensor”: “sensor1”, “temperature”: 22, “humidity”: 80},{“sensor”: “sensor2”, “temperature”: 22, “humidity”: 65}]’

• An array with two objects
• An object with two arrays

Q3 – The JSON string consists of:

‘[{“sensor”: “sensor1″,”temperature”: 24, “humidity”: 69},{“sensor”: sensor2, “temperature”: 22, “humidity”: 65}]’

How to Create Complex JSON Strings Using the Online JSON Editor

You can use the online JSON editor tool for creating more complex JSON structures.

To illustrate how to use the JSON editor Tool we are going to create a JSON string with an array of 3 objects.The objects are:

Object 1 contains the following key/value pairs:

name=John,DOB=1/1/2000,Age=20

Object 2 contains the following key/value pairs:

name=Jim,DOB=21/2/1990,Age=30

Object 3 contains the following key/value pairs: name=Jane,DOB=6/11/1958,Age=61

Video- Using the Online JSON editor

Converting JSON to JavaScript and Extracting Data

Although you can extract data from a JSON string using JSONata the most common method is to convert it to a JavaScript object, and that is what we cover here.

To convert a JSON string to a JavaScript object you use the JSON parse function.

Object= JSON.parse(JSONstring)

Once the JSON data is placed into a JavaScript Object you can now use normal JavaScript methods and functions to extract elements of that data.

We will start using the simple JavaScript object we saw earlier.

var power={voltage: “240”,current: “1”}

To extract the voltage we use either the dot form or the square bracket.

var voltage=power.voltage or

var voltage=power[“voltage”]

var data=[ { sensor: ‘sensor1’, temperature: 21, humidity: 67 },

[{ sensor: ‘sensor2’, temperature: 22, humidity: 62 } ]

This time we have an array of two objects.

var sensor= data[0].sensor

Returns sensor1

var sensor= data[1].hunidity

Note: Arrays start at 0

This time we have an object containing an array :

sensor={ results: [ 1, 21, 34, 21 ] }

To access the 2 element in the results array we use:

var r= sensor.results[1]

Using the Node.js Command Line

When working on decoding JSON strings I often find it easier to paste the string into a node.js command line and decode it there.

The examples below show screen shots using the node.js command prompt for converting to and from JSON strings to JavaScript Objects.

JSON And JavaScript

There are two functions that are used for converting data from JavaScript to JSON and from JSON to JavaScript . They are:

• JSON.stringify (JavaScript object)
• JSON.parse (JSON string)

Example: JavaScript Array to JSON String

Example: JSON to JavaScript

Below shows the JSON strings b and d being converted back to JavaScript.

Answers to Questions

Question 1: D

Question 2: A

Question 2: B – Missing quotes on sensor2 .

JSON is used extensively in web and IOT applications and is a very easy method for storing and transferring complex data as text.

All major programming languages have functions for encoding and decoding JSON data

JSON WorkBook

If you want more practise with working with JSON and JavaScript then you might be interested in my workbook.

• JSON formatter and Validator
• Another JSON Formatter and Validator
• Using HTTP APIs for IOT
• Using MQTT APIs for IOT

Related Tutorials

• Working with JSON Data And JavaScript Objects in Node-Red
• How to Send and Receive JSON Data Over MQTT with Python
• Simple Python MQTT Topic Logger
• Simple Python MQTT Data Logger
• Beginners Guide to Data and Character Encoding

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format json you can try https://www.jsonformatting.com/

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JSON Editor Online

JSON Editor Online is the original and most copied JSON Editor on the web. Use it to view, edit, format, repair, compare, query, transform, validate, and share your JSON data.

About JSON Editor Online

JSON Editor Online is a versatile, high quality tool to edit and process your JSON data. It is one of the best and most popular tools around, has a high user satisfaction, and is completely free. The editor offers all your need in one place: from formatting and beautifying your JSON data to comparing JSON documents or querying your JSON data. JSON is the most used data format between servers and browser, and JSON Editor Online is an indispensable tool for frontend and backend developers working with JSON data in their daily life.

JSON Editor Online is developed and maintained by Jos de Jong , an enthusiastic, passionate senior software engineer with 20+ years of experience in the field. Jos created the editor out of personal needs, working with JSON based API's and databases on a daily basis as a full-stack engineer.

JSON Editor Online offers the following features :

• Online JSON editor and JSON viewer
• Text mode, tree mode, and table mode
• JSON formatter and JSON beautifier
• Query and transform JSON
• Compare JSON documents
• Repair JSON documents
• JSON Schema validation
• Load, save and share JSON online
• JSON to CSV converter
• CSV to JSON converter
• Process large JSON files
• Offline JSON Editor

Current version: 7.0.2

Search and Replace in Table mode

The table mode has become a lot more powerful today: it now has a search and replace feature just like tree mode.

Smart JSON Formatting

JSON Editor Online introduces a new feature: Smart JSON Formatting , available in text mode. This is a new way to format JSON data in a readable but more compact way.

Ad-free subscription

JSON Editor Online now offers an alternative to ads: an ad-free subscription! Get more screen space, a faster application, less data traffic, and an experience completely free of tracking. You'll also get extras: you can store your documents privately in the cloud.

Read more in the article "Ad-free JSON Editor Online experience" , or go to the pricing page to subscribe .

Developer Tools

We all love to hack around in the Developer Console, don't we? With this latest update, you can now access the left and right editor via editorLeft and editorRight , and you can access the JSON utility libraries lodash , jsonrepair , Ajv , jmespath , and immutable-json-patch in the Developer Console. Just open your Developer Console via F12 or Ctrl+Shift+I and follow the instructions there.

JSON Editor Online just promoted to a fully capable CSV editor. The editor already had a powerful table view and could export to CSV. Now you can also import CSV, making the CSV support complete.

You can import a CSV file via the menu "Open", "Import CSV", or by dropping a CSV file on the editor.

Read more in the article "Convert JSON to CSV using JSON Editor Online" .

Want to know more? An overview describing the full history is available in the Changelog .

Frequently asked questions (FAQ)

How do i edit a json file.

Copy and paste your JSON file in the JSON editor, or load it from disk via the menu or via drag-and-drop. Then, you can edit the contents similar to how you use any text editor: enter new content with your keyboard, and right-click to open a context menu with actions like copy/paste, insert, remove. You can learn more in the Documentation .

How do I format a JSON file?

You can use the editor as a json formatter. In code mode, you can paste a JSON file in the editor, and click the "Format" button from the menu. In tree mode, you can just paste the file and copy it again: the contents will automatically be formatted. Alternatively, you can also use the "Copy formatted" button from the menu to be done in one click. Read more.

How do I beautify JSON data?

Format JSON is the same as beautify JSON : you make your JSON file readable by styling it with white spacing, newlines, and indentation. In short: paste your JSON file, then click the "Format" button in code mode, or select "Copy formatted" from the menu. This is how you make your JSON pretty. Read more.

Can I use JSON editor as a JSON cleaner?

Yes, definitely! Cleaning JSON is the same as "beautifying" or "formatting" JSON: you make the JSON data neatly readable. So you can use JSON Editor Online as a JSON cleaner by opening your document and then clicking the "Format" button.

How do I query JSON data?

You can query JSON data by clicking the "Transform" button from the menu or between the two panels. This will open a modal where you can write a query, see a preview, and then transform JSON data. Read more.

How do I compare JSON files?

You can compare JSON files by opening them in the left and right panel of the editor. Click the "Compare" button in the "Differences" section between the two panels, and make sure you switch both panels to "Tree" mode. All JSON differences will be highlighted. Read more.

How do I repair JSON data?

Just drop your data in JSON Editor Online. In many cases it will automatically repair the data for you, and if not possible, it will point you to the place where the issue is and assist you with repairing it. Read more.

How do I fix JSON format errors?

Open your JSON file in JSON Editor Online, then click the "Format" button from the menu when in text mode. Read more in the docs about JSON repair , or read more about common JSON issues and how to fix them .

How do I check if a JSON file is valid?

Simply open the JSON file in JSON Editor Online to see if the document itself is valid. If not, the editor will point out the error and if possible offer to auto-repair the document .

What is a JSON validator?

A JSON validator verifies whether your JSON document adheres to the JSON specification . On top of this, you can use a JSON schema validator to validate whether the contents of your document adheres to a specified schema.

What is a JSON beautifier and validator?

Beautifying JSON is neatly formatting its contents with indentation and new lines to make it better readable. Software developers often validate JSON and then format it in one go in order to inspect the document.

Why use the editor as a JSON Validator?

Using an online JSON editor is useful for validating, repairing , formatting , and querying JSON data on the fly. When working with JSON configuration files in a project though, it may be handier to use your own IDE. There are various categories of JSON tools , each with their pros and cons. What is best to use depends on your use case.

How do I validate my JSON data against a JSON Schema?

Open your JSON file in the editor. From the menu, select "Options", "JSON Schema". A modal will open where you can configure your JSON schema. Read more.

How can I convert JSON to CSV?

Open your JSON file in the editor. From the menu, select "Save", "Export to CSV". A modal will open where you see a preview and can save the CSV data as file or copy it to your clipboard. Read more.

What is the best JSON editor?

A survey shows that 85% of the people using JSON Editor Online are highly satisfied with it. They are overwhelmingly positive and call it the best JSON formatter and editor. They are very positive about the quality of the all-in-one editor, which has proven itself useful for millions of users for more than 10 years already. People also love the fact that this is a free JSON editor.

What is the best JSON formatter?

This question is more or less the same as the previous question "What is the best JSON editor?". JSON Editor Online is also a JSON formatter and JSON beautifier. Formatting is just one of the many features it offers.

Are JSON Formatters safe?

In general, yes, you don't have to worry. JSON Editor Online takes all possible measures: it enforces a secure HTTPS connection and keeps all used software actively up to date. All you do stays inside your browser, and no data is shared anywhere except when you save a document in the cloud. Cloud documents are publicly accessible for anyone who has the document id, so make sure you do not save documents with sensitive information in the cloud.

Does it have a dark mode?

Yes! JSON Editor Online has a light mode and a dark mode. You can toggle this in the main menu on top.

Can I open large JSON files?

Yes! JSON Editor Online can work with large files up to 512 MB 🚀.

Can I save documents privately in the cloud?

Yes. When you have an active subscription , you can save private documents in the cloud. A private document is only accessible to its owner (you), when you are logged in.

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JSON Tutorial

• JSON Introduction
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• Interesting facts about JSON

JSON stands for JavaScript Object Notation . It is a format for structuring data. This format is used by different web applications to communicate with each other. It is the replacement of the XML data exchange format. It is easier to structure the data compared to XML . It supports data structures like arrays and objects, and JSON documents that are rapidly executed on the server.

Within this JSON tutorial , you will be starting with the basics of JSON Syntax including objects, arrays, values, keys, and string formats, and going into the advanced JSON topics including parsing JSON in various programming languages, using JSON for web APIs, and data handling of large JSON datasets.

By the end, you’ll gain a solid understanding of JSON and how to use it effectively

What is JSON?

JSON , short for JavaScript Object Notation , makes sharing data simple and straightforward. Created by Douglas Crockford, it’s designed for easy reading and writing by humans, and easy parsing and generating by computers. Its main goal was to make a text format that’s good at showing simple data like lists and text, and really useful for websites.

JSON is special because it’s very clear and easy to use, and it uses a “.json” file ending to show that a file is in this format. This makes JSON great for both people and programs to work with.

Why use JSON?

JSON is used in a variety of contexts, primarily for data interchange between servers and web applications: Here are the reasons:

• Language Independence : Though it is derived from a subset of JavaScript, yet it is Language independent . Thus, the code for generating and parsing JSON data can be written in any other programming language.
• Human-readable Format : It is Human-readable and writable.
• Lightweight Data Interchange Format : It is a lightweight text-based data interchange format which means, it is simpler to read and write when compared to XML.
• Easy Parsing and Generation : Allows conversion of parse JSON data into native data structures and vice versa easily, simplifying the process of working with data.

JSON Syntax

In JSON , data is primarily stored in two structures: objects and arrays . Here’s a detailed breakdown of the syntax for each:

1. Using ‘Objects’

• Objects in JSON are collections of key/value pairs enclosed in curly braces {} .
• Each key is a string (enclosed in double quotes " ) followed by a colon : , and the key/value pairs are separated by commas (,) .

2. Using ‘Arrays’

• Arrays are ordered lists of values, enclosed in square brackets [] .
• Values within an array are separated by commas (,) .

Basic JSON Example

Getting started with json.

Before getting started with JSON, you must have basic programming knowledge and familiarity with data structures like objects and arrays.

Useful JSON Tools

Effortlessly transform JSON to CSV with our user-friendly tools! Simplify your data handling and analysis. Use our JSON Formatter and Validator to format and beautify your JSON code.

Advantages of JSON

• It stores all the data in an array so that data transfer makes easier. That’s why it is the best for sharing data of any size even audio, video, etc.
• Its syntax is very small, easy, and light-weighted that’s the reason it executes and responds in a faster way.
• It has a wide range for browser support compatibility with the operating systems. It doesn’t require much effort to make it all browser compatible.
• On the server-side parsing is the most important part that developers want. If the parsing will be fast on the server side then the user can get a fast response, so in this case, JSON server-side parsing is the strong point compared to others.

Limitations of JSON

• The main limitation is that there is no error handling . If there was a slight mistake in the script then you will not get the structured data.
• It becomes quite dangerous when you used it with some unauthorized browsers . Like JSON service return a JSON file wrapped in a function call that has to be executed by the browsers if the browsers are unauthorized then your data can be hacked.
• It has limited supported tools that we can use during the development.

Frequently Asked Questions About JSON

What is the full-form json.

“JSON” stands for “ JavaScript Object Notation ” and is a lightweight data format used for easy exchange of data between different systems.

What is JSON used for?

JSON is used for data interchange between servers and web applications, configuration files, and storing structured data.

What is the basic structure of JSON?

JSON structure is made of k ey-value pairs within objects {} and ordered lists of values within arrays [] .

What are the types of JSON?

In JSON, “types” typically refer to the values that can be represented within the JSON format. JSON supports the following data types: String : A sequence of characters, enclosed in double quotes. Example : "Hello, world!" Number : Integer or floating-point numbers. Example : 42 or 3.14 Object : An unordered collection of key-value pairs, enclosed in curly braces {} . Example : {"name": "John", "age": 30} Array : An ordered list of values, enclosed in square brackets [] . Example : ["apple", "banana", "cherry"] Boolean : Represents true or false values. Example : true or false Null : Represents a null value, indicating the absence of any value. Example : null

Is JSON Slowing Down Our Apps?

In some case, especially for large data sets. JSON is easy to read but larger and requires more processing than some alternatives

Why is JSON preferred over XML?

JSON is more lightweight, easier to read and write, and faster to parse than XML.

What are the alternative of JSON?

The best choice depends on your needs: data size, readability, and development time. Here are some alternatives of json: Protocol Buffers : Smaller, faster format, good for various languages. MessagePack : Efficient for real-time data exchange. Custom Binary Formats : Fastest option, but requires more development effort.

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• JSON to XML
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• JSON Full Form

JSON Formatter

JSON Formatter and JSON Validator help to auto format JSON and validate your JSON text. It also provides a tree view that helps to navigate your formatted JSON data.

• It helps to validate JSON online with Error Messages.
• It's the only JSON tool that shows the image on hover on Image URL in a tree view.
• It's also a JSON Beautifier that supports indentation levels: 2 spaces, 3 spaces, and 4 spaces.
• Supports Printing of JSON Data.
• JSON File Formatter provides functionality to upload JSON file and download formatted JSON File. This functionality helps to format json file.
• 95% of API Uses JSON to transfer data between client and server. This tools can works as API formatter.
• Supports JSON Graph View of JSON String which works as JSON debugger or corrector and can format Array and Object.
• Stores data locally for the last JSON Formatted in Browser's Local Storage. This can be used as notepad++ / Sublime / VSCode alternative of JSON beautification.
• This JSON online formatter can also work as JSON Lint .
• Use Auto switch to turn auto update on or off for beautification.
• It uses $.parseJSON and JSON.stringify to beautify JSON easy for a human to read and analyze.
• Download JSON, once it's created or modified and it can be opened in Notepad++, Sublime, or VSCode alternative.

Know more about JSON :

• How to Create JSON File?
• What is JSON?
• JSON Example with all data types including JSON Array.
• Python Pretty Print JSON
• Read JSON File Using Python
• Validate JSON using PHP
• Python Load Json From File

Online JSON Formatter and Online JSON Validator provide JSON converter tools to convert JSON to XML , JSON to CSV , and JSON to YAML also JSON Editor , JSONLint, JSON Checker, and JSON Cleaner.

Free JSON Formatting Online and JSON Validator work well in Windows, Mac, Linux, Chrome, Firefox, Safari, and Edge.

JSON Example:

Json validator.

JSON Validator Online checks the integrity/syntax of the JSON data based on JavaScript Object Notation (JSON) Data Interchange Format Specifications (RFC).

• It's super easy to find the error when line numbers are highlighted with an in-detail error description.
• Use Screwdriver icon to as JSON Fixer to repair the error.
• To validate JSON you just need internet and no need to install any software.
• Your JSON Data gets validated in the browser itself.

This JSON Lint tool provides fast and without sign up, user can checks the JSON data's validity.

Why JSON FORMATTER?

Why format json online, how do i format a json file, how to use json formatter with url, is login required to save json data, have you accidentally saved your json data, load external data, save online.

Instantly generate C# models and helper methods from JSON.

Generate C# classes with Json.NET attributes from JSON, JSON Schema, and GraphQL queries.

quicktype is fluent in

• Effect Schema
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© 2024 all rights reserved

🥎 WCWS begins | Noon ET

DIII softball finals

👀 Every DI baseball regional, previewed

🥵 Toughest DI baseball regional

NCAA | May 26, 2024

Sixteen regional sites selected for the 2024 ncaa di baseball championship.

INDIANAPOLIS – The NCAA Division I Baseball Committee announced the 16 regional sites for the 77th annual NCAA Division I Baseball Championship.

The 16 regional sites, with host institutions and records are as follows: 

• Athens, Georgia – Georgia (39-15)
• Chapel Hill, North Carolina – North Carolina (42-13)
• Charlottesville, Virginia – Virginia (41-15)
• Clemson, South Carolina – Clemson (41-14)
• Bryan-College Station, Texas – Texas A&M (44-13)
• Corvallis, Oregon – Oregon State (42-14)
• Fayetteville, Arkansas – Arkansas (43-14)
• Greenville, North Carolina – East Carolina (43-15)
• Knoxville, Tennessee – Tennessee (50-11) 
• Lexington, Kentucky – Kentucky (40-14) 
• Norman, Oklahoma – Oklahoma (37-19) 
• Raleigh, North Carolina – NC State (33-20) 
• Santa Barbara, California – UC Santa Barbara (42-12) 
• Stillwater, Oklahoma – Oklahoma State (40-17) 
• Tallahassee, Florida – Florida State (42-15)
• Tucson, Arizona – Arizona (36-21)

By being awarded a regional, all 16 host institutions have also been selected to the 64-team championship field.

Each regional field features four teams, playing in a double-elimination format. All 16 regionals are scheduled to be conducted from Friday, May 31 to Monday, June 3 (if necessary).

UC Santa Barbara is hosting for only the second time and for the first time in Santa Barbara (it hosted at a neutral site at Lake Elsinore in 2015).

Florida State is hosting a baseball regional for the 36th time in tournament history, the most by any school. Oklahoma is hosting for the first time since 2010.

Arkansas, Clemson, Kentucky, Oklahoma State and Virginia each hosted regionals in 2023 and Oklahoma State is hosting for a third consecutive season.

The full 64-team field, top-16 national seeds, first-round regional pairings and site assignments will be announced at Noon (ET), on Monday, May 27. The one-hour program will be shown live on ESPN2. The committee will set the entire 64-team bracket through both the super regionals and the first round of the Men’s College World Series and will not reseed the field after play begins. 

Selection of the eight super regional hosts will be announced on www.NCAA.com/mcws , Tuesday, June 4 at 10 a.m. (ET). 

There are 30 Division I Conferences which will receive an automatic berth in the field of 64, along with 34 at-large selections. The Men’s College World Series begins play Friday, June 14, at Charles Schwab Field Omaha in Omaha, Nebraska.  

• Every 2024 college baseball regional, previewed

This week in DII sports: 8 for Cary — What to know about the DII baseball championship finals

The toughest regional in the 2024 DI baseball bracket, according to Michella Chester

• Championship Info
• Game Program

Men's College World Series

• 🗓️ 2024 schedule
• 🔮 Future dates
• 🤔 How the MCWS works
• 🏆 Programs with the most MCWS titles
• 💪 Coaches with the most MCWS wins
• ⚾ Every champion in tournament history

Everything you need to know about how the Men's College World Series works

Di baseball news.

• Michella Chester: The toughest regional in the DI baseball bracket
• The toughest regional in the 2024 DI baseball bracket
• Arkansas wins the best college baseball stadium vote by the fans
• How the Men's College World Series works

College baseball career home run leaders

Here are the baseball programs with the most Men's College World Series titles

The 7 longest home runs in MCWS history (that we know of)

Follow di baseball.

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Microsoft fabric updates blog.

Microsoft Fabric May 2024 Update

• Monthly Update

Welcome to the May 2024 update.  

Here are a few, select highlights of the many we have for Fabric. You can now ask Copilot questions about data in your model, Model Explorer and authoring calculation groups in Power BI desktop is now generally available, and Real-Time Intelligence provides a complete end-to-end solution for ingesting, processing, analyzing, visualizing, monitoring, and acting on events.

There is much more to explore, please continue to read on. 

Microsoft Build Announcements

At Microsoft Build 2024, we are thrilled to announce a huge array of innovations coming to the Microsoft Fabric platform that will make Microsoft Fabric’s capabilities even more robust and even customizable to meet the unique needs of each organization. To learn more about these changes, read the “ Unlock real-time insights with AI-powered analytics in Microsoft Fabric ” announcement blog by Arun Ulag.

Fabric Roadmap Update

Last October at the Microsoft Power Platform Community Conference we  announced the release of the Microsoft Fabric Roadmap . Today we have updated that roadmap to include the next semester of Fabric innovations. As promised, we have merged Power BI into this roadmap to give you a single, unified road map for all of Microsoft Fabric. You can find the Fabric Roadmap at  https://aka.ms/FabricRoadmap .

We will be innovating our Roadmap over the coming year and would love to hear your recommendation ways that we can make this experience better for you. Please submit suggestions at  https://aka.ms/FabricIdeas .

Earn a discount on your Microsoft Fabric certification exam!  

We’d like to thank the thousands of you who completed the Fabric AI Skills Challenge and earned a free voucher for Exam DP-600 which leads to the Fabric Analytics Engineer Associate certification.   

If you earned a free voucher, you can find redemption instructions in your email. We recommend that you schedule your exam now, before your discount voucher expires on June 24 th . All exams must be scheduled and completed by this date.    

If you need a little more help with exam prep, visit the Fabric Career Hub which has expert-led training, exam crams, practice tests and more.  

Missed the Fabric AI Skills Challenge? We have you covered. For a limited time , you could earn a 50% exam discount by taking the Fabric 30 Days to Learn It Challenge .  

Modern Tooltip now on by Default

Matrix layouts, line updates, on-object interaction updates, publish to folders in public preview, you can now ask copilot questions about data in your model (preview), announcing general availability of dax query view, copilot to write and explain dax queries in dax query view public preview updates, new manage relationships dialog, refreshing calculated columns and calculated tables referencing directquery sources with single sign-on, announcing general availability of model explorer and authoring calculation groups in power bi desktop, microsoft entra id sso support for oracle database, certified connector updates, view reports in onedrive and sharepoint with live connected semantic models, storytelling in powerpoint – image mode in the power bi add-in for powerpoint, storytelling in powerpoint – data updated notification, git integration support for direct lake semantic models.

• Editor’s pick of the quarter
• New visuals in AppSource
• Financial Reporting Matrix by Profitbase
• Horizon Chart by Powerviz

Milestone Trend Analysis Chart by Nova Silva

• Sunburst Chart by Powerviz
• Stacked Bar Chart with Line by JTA

Fabric Automation

Streamlining fabric admin apis, microsoft fabric workload development kit, external data sharing, apis for onelake data access roles, shortcuts to on-premises and network-restricted data, copilot for data warehouse.

• Unlocking Insights through Time: Time travel in Data warehouse

Copy Into enhancements

Faster workspace resource assignment powered by just in time database attachment, runtime 1.3 (apache spark 3.5, delta lake 3.1, r 4.3.3, python 3.11) – public preview, native execution engine for fabric runtime 1.2 (apache spark 3.4) – public preview , spark run series analysis, comment @tagging in notebook, notebook ribbon upgrade, notebook metadata update notification, environment is ga now, rest api support for workspace data engineering/science settings, fabric user data functions (private preview), introducing api for graphql in microsoft fabric (preview), copilot will be enabled by default, the ai and copilot setting will be automatically delegated to capacity admins, abuse monitoring no longer stores your data, real-time hub, source from real-time hub in enhanced eventstream, use real-time hub to get data in kql database in eventhouse, get data from real-time hub within reflexes, eventstream edit and live modes, default and derived streams, route streams based on content in enhanced eventstream, eventhouse is now generally available, eventhouse onelake availability is now generally available, create a database shortcut to another kql database, support for ai anomaly detector, copilot for real-time intelligence, eventhouse tenant level private endpoint support, visualize data with real-time dashboards, new experience for data exploration, create triggers from real-time hub, set alert on real-time dashboards, taking action through fabric items, general availability of the power query sdk for vs code, refresh the refresh history dialog, introducing data workflows in data factory, introducing trusted workspace access in fabric data pipelines.

• Introducing Blob Storage Event Triggers for Data Pipelines
• Parent/child pipeline pattern monitoring improvements

Fabric Spark job definition activity now available

Hd insight activity now available, modern get data experience in data pipeline.

Power BI tooltips are embarking on an evolution to enhance their functionality. To lay the groundwork, we are introducing the modern tooltip as the new default , a feature that many users may already recognize from its previous preview status. This change is more than just an upgrade; it’s the first step in a series of remarkable improvements. These future developments promise to revolutionize tooltip management and customization, offering possibilities that were previously only imaginable. As we prepare for the general availability of the modern tooltip, this is an excellent opportunity for users to become familiar with its features and capabilities. 

Discover the full potential of the new tooltip feature by visiting our dedicated blog . Dive into the details and explore the comprehensive vision we’ve crafted for tooltips, designed to enhance your Power BI experience. 

We’ve listened to our community’s feedback on improving our tabular visuals (Table and Matrix), and we’re excited to initiate their transformation. Drawing inspiration from the familiar PivotTable in Excel , we aim to build new features and capabilities upon a stronger foundation. In our May update, we’re introducing ‘ Layouts for Matrix .’ Now, you can select from compact , outline , or tabular layouts to alter the arrangement of components in a manner akin to Excel. 

As an extension of the new layout options, report creators can now craft custom layout patterns by repeating row headers. This powerful control, inspired by Excel’s PivotTable layout, enables the creation of a matrix that closely resembles the look and feel of a table. This enhancement not only provides greater flexibility but also brings a touch of Excel’s intuitive design to Power BI’s matrix visuals. Only available for Outline and Tabular layouts.

To further align with Excel’s functionality, report creators now have the option to insert blank rows within the matrix. This feature allows for the separation of higher-level row header categories, significantly enhancing the readability of the report. It’s a thoughtful addition that brings a new level of clarity and organization to Power BI’s matrix visuals and opens a path for future enhancements for totals/subtotals and rows/column headers. 

We understand your eagerness to delve deeper into the matrix layouts and grasp how these enhancements fulfill the highly requested features by our community. Find out more and join the conversation in our dedicated blog , where we unravel the details and share the community-driven vision behind these improvements. 

Following last month’s introduction of the initial line enhancements, May brings a groundbreaking set of line capabilities that are set to transform your Power BI experience: 

• Hide/Show lines : Gain control over the visibility of your lines for a cleaner, more focused report. 
• Customized line pattern : Tailor the pattern of your lines to match the style and context of your data. 
• Auto-scaled line pattern : Ensure your line patterns scale perfectly with your data, maintaining consistency and clarity. 
• Line dash cap : Customize the end caps of your customized dashed lines for a polished, professional look. 
• Line upgrades across other line types : Experience improvements in reference lines, forecast lines, leader lines, small multiple gridlines, and the new card’s divider line. 

These enhancements are not to be missed. We recommend visiting our dedicated blog for an in-depth exploration of all the new capabilities added to lines, keeping you informed and up to date. 

This May release, we’re excited to introduce on-object formatting support for Small multiples , Waterfall , and Matrix visuals. This new feature allows users to interact directly with these visuals for a more intuitive and efficient formatting experience. By double-clicking on any of these visuals, users can now right-click on the specific visual component they wish to format, bringing up a convenient mini-toolbar. This streamlined approach not only saves time but also enhances the user’s ability to customize and refine their reports with ease. 

We’re also thrilled to announce a significant enhancement to the mobile reporting experience with the introduction of the pane manager for the mobile layout view. This innovative feature empowers users to effortlessly open and close panels via a dedicated menu, streamlining the design process of mobile reports. 

We recently announced a public preview for folders in workspaces, allowing you to create a hierarchical structure for organizing and managing your items. In the latest Desktop release, you can now publish your reports to specific folders in your workspace.  

When you publish a report, you can choose the specific workspace and folder for your report. The interface is simplistic and easy to understand, making organizing your Power BI content from Desktop better than ever. 

To publish reports to specific folders in the service, make sure the “Publish dialogs support folder selection” setting is enabled in the Preview features tab in the Options menu. 

Learn more about folders in workspaces.   

We’re excited to preview a new capability for Power BI Copilot allowing you to ask questions about the data in your model! You could already ask questions about the data present in the visuals on your report pages – and now you can go deeper by getting answers directly from the underlying model. Just ask questions about your data, and if the answer isn’t already on your report, Copilot will then query your model for the data instead and return the answer to your question in the form of a visual! 

We’re starting this capability off in both Edit and View modes in Power BI Service. Because this is a preview feature, you’ll need to enable it via the preview toggle in the Copilot pane. You can learn more about all the details of the feature in our announcement post here! (will link to announcement post)  

We are excited to announce the general availability of DAX query view. DAX query view is the fourth view in Power BI Desktop to run DAX queries on your semantic model.  

DAX query view comes with several ways to help you be as productive as possible with DAX queries. 

• Quick queries. Have the DAX query written for you from the context menu of tables, columns, or measures in the Data pane of DAX query view. Get the top 100 rows of a table, statistics of a column, or DAX formula of a measure to edit and validate in just a couple clicks! 
• DirectQuery model authors can also use DAX query view. View the data in your tables whenever you want! 
• Create and edit measures. Edit one or multiple measures at once. Make changes and see the change in action in a DA query. Then update the model when you are ready. All in DAX query view! 
• See the DAX query of visuals. Investigate the visuals DAX query in DAX query view. Go to the Performance Analyzer pane and choose “Run in DAX query view”. 
• Write DAX queries. You can create DAX queries with Intellisense, formatting, commenting/uncommenting, and syntax highlighting. And additional professional code editing experiences such as “Change all occurrences” and block folding to expand and collapse sections. Even expanded find and replace options with regex. 

Learn more about DAX query view with these resources: 

• Deep dive blog: https://powerbi.microsoft.com/blog/deep-dive-into-dax-query-view-and-writing-dax-queries/  
• Learn more: https://learn.microsoft.com/power-bi/transform-model/dax-query-view  
• Video: https://youtu.be/oPGGYLKhTOA?si=YKUp1j8GoHHsqdZo  

DAX query view includes an inline Fabric Copilot to write and explain DAX queries, which remains in public preview. This month we have made the following updates. 

• Run the DAX query before you keep it . Previously the Run button was disabled until the generated DAX query was accepted or Copilot was closed. Now you can Run the DAX query then decide to Keep or Discard the DAX query. 

2. Conversationally build the DAX query. Previously the DAX query generated was not considered if you typed additional prompts and you had to keep the DAX query, select it again, then use Copilot again to adjust. Now you can simply adjust by typing in additional user prompts.   

3. Syntax checks on the generated DAX query. Previously there was no syntax check before the generated DAX query was returned. Now the syntax is checked, and the prompt automatically retried once. If the retry is also invalid, the generated DAX query is returned with a note that there is an issue, giving you the option to rephrase your request or fix the generated DAX query. 

4. Inspire buttons to get you started with Copilot. Previously nothing happened until a prompt was entered. Now click any of these buttons to quickly see what you can do with Copilot! 

Learn more about DAX queries with Copilot with these resources: 

• Deep dive blog: https://powerbi.microsoft.com/en-us/blog/deep-dive-into-dax-query-view-with-copilot/  
• Learn more: https://learn.microsoft.com/en-us/dax/dax-copilot  
• Video: https://www.youtube.com/watch?v=0kE3TE34oLM  

We are excited to introduce you to the redesigned ‘Manage relationships’ dialog in Power BI Desktop! To open this dialog simply select the ‘Manage relationships’ button in the modeling ribbon.

Once opened, you’ll find a comprehensive view of all your relationships, along with their key properties, all in one convenient location. From here you can create new relationships or edit an existing one.

Additionally, you have the option to filter and focus on specific relationships in your model based on cardinality and cross filter direction. 

Learn more about creating and managing relationships in Power BI Desktop in our documentation . 

Ever since we released composite models on Power BI semantic models and Analysis Services , you have been asking us to support the refresh of calculated columns and tables in the Service. This month, we have enabled the refresh of calculated columns and tables in Service for any DirectQuery source that uses single sign-on authentication. This includes the sources you use when working with composite models on Power BI semantic models and Analysis Services.  

Previously, the refresh of a semantic model that uses a DirectQuery source with single-sign-on authentication failed with one of the following error messages: “Refresh is not supported for datasets with a calculated table or calculated column that depends on a table which references Analysis Services using DirectQuery.” or “Refresh over a dataset with a calculated table or a calculated column which references a Direct Query data source is not supported.” 

Starting today, you can successfully refresh the calculated table and calculated columns in a semantic model in the Service using specific credentials as long as: 

• You used a shareable cloud connection and assigned it and/or.
• Enabled granular access control for all data connection types.

Here’s how to do this: 

• Create and publish your semantic model that uses a single sign-on DirectQuery source. This can be a composite model but doesn’t have to be. 
• In the semantic model settings, under Gateway and cloud connections , map each single sign-on DirectQuery connection to a specific connection. If you don’t have a specific connection yet, select ‘Create a connection’ to create it: 

• If you are creating a new connection, fill out the connection details and click Create , making sure to select ‘Use SSO via Azure AD for DirectQuery queries: 

• Finally, select the connection for each single sign-on DirectQuery source and select Apply : 

2. Either refresh the semantic model manually or plan a scheduled refresh to confirm the refresh now works successfully. Congratulations, you have successfully set up refresh for semantic models with a single sign-on DirectQuery connection that uses calculated columns or calculated tables!

We are excited to announce the general availability of Model Explorer in the Model view of Power BI, including the authoring of calculation groups. Semantic modeling is even easier with an at-a-glance tree view with item counts, search, and in context paths to edit the semantic model items with Model Explorer. Top level semantic model properties are also available as well as the option to quickly create relationships in the properties pane. Additionally, the styling for the Data pane is updated to Fluent UI also used in Office and Teams.  

A popular community request from the Ideas forum, authoring calculation groups is also included in Model Explorer. Calculation groups significantly reduce the number of redundant measures by allowing you to define DAX formulas as calculation items that can be applied to existing measures. For example, define a year over year, prior month, conversion, or whatever your report needs in DAX formula once as a calculation item and reuse it with existing measures. This can reduce the number of measures you need to create and make the maintenance of the business logic simpler.  

Available in both Power BI Desktop and when editing a semantic model in the workspace, take your semantic model authoring to the next level today!  

Learn more about Model Explorer and authoring calculation groups with these resources: 

• Use Model explorer in Power BI (preview) – Power BI | Microsoft Learn  
• Create calculation groups in Power BI (preview) – Power BI | Microsoft Learn  

Data connectivity  

We’re happy to announce that the Oracle database connector has been enhanced this month with the addition of Single Sign-On support in the Power BI service with Microsoft Entra ID authentication.  

Microsoft Entra ID SSO enables single sign-on to access data sources that rely on Microsoft Entra ID based authentication. When you configure Microsoft Entra SSO for an applicable data source, queries run under the Microsoft Entra identity of the user that interacts with the Power BI report. 

We’re pleased to announce the new and updated connectors in this release:   

• [New] OneStream : The OneStream Power BI Connector enables you to seamlessly connect Power BI to your OneStream applications by simply logging in with your OneStream credentials. The connector uses your OneStream security, allowing you to access only the data you have based on your permissions within the OneStream application. Use the connector to pull cube and relational data along with metadata members, including all their properties. Visit OneStream Power BI Connector to learn more. Find this connector in the other category. 
• [New] Zendesk Data : A new connector developed by the Zendesk team that aims to go beyond the functionality of the existing Zendesk legacy connector created by Microsoft. Learn more about what this new connector brings. 
• [New] CCH Tagetik 
• [Update] Azure Databricks  

Are you interested in creating your own connector and publishing it for your customers? Learn more about the Power Query SDK and the Connector Certification program .   

Last May, we announced the integration between Power BI and OneDrive and SharePoint. Previously, this capability was limited to only reports with data in import mode. We’re excited to announce that you can now seamlessly view Power BI reports with live connected data directly in OneDrive and SharePoint! 

When working on Power BI Desktop with a report live connected to a semantic model in the service, you can easily share a link to collaborate with others on your team and allow them to quickly view the report in their browser. We’ve made it easier than ever to access the latest data updates without ever leaving your familiar OneDrive and SharePoint environments. This integration streamlines your workflows and allows you to access reports within the platforms you already use. With collaboration at the heart of this improvement, teams can work together more effectively to make informed decisions by leveraging live connected semantic models without being limited to data only in import mode.  

Utilizing OneDrive and SharePoint allows you to take advantage of built-in version control, always have your files available in the cloud, and utilize familiar and simplistic sharing.  

While you told us that you appreciate the ability to limit the image view to only those who have permission to view the report, you asked for changes for the “Public snapshot” mode.   

To address some of the feedback we got from you, we have made a few more changes in this area.  

• Add-ins that were saved as “Public snapshot” can be printed and will not require that you go over all the slides and load the add-ins for permission check before the public image is made visible. 
• You can use the “Show as saved image” on add-ins that were saved as “Public snapshot”. This will replace the entire add-in with an image representation of it, so the load time might be faster when you are presenting your presentation. 

Many of us keep presentations open for a long time, which might cause the data in the presentation to become outdated.  

To make sure you have in your slides the data you need, we added a new notification that tells you if more up to date data exists in Power BI and offers you the option to refresh and get the latest data from Power BI. 

Developers 

Direct Lake semantic models are now supported in Fabric Git Integration , enabling streamlined version control, enhanced collaboration among developers, and the establishment of CI/CD pipelines for your semantic models using Direct Lake. 

Learn more about version control, testing, and deployment of Power BI content in our Power BI implementation planning documentation: https://learn.microsoft.com/power-bi/guidance/powerbi-implementation-planning-content-lifecycle-management-overview  

Visualizations 

Editor’s pick of the quarter .

– Animator for Power BI     Innofalls Charts     SuperTables     Sankey Diagram for Power BI by ChartExpo     Dynamic KPI Card by Sereviso     Shielded HTML Viewer     Text search slicer  

New visuals in AppSource 

Mapa Polski – Województwa, Powiaty, Gminy   Workstream   Income Statement Table  

Gas Detection Chart  

Seasonality Chart   PlanIn BI – Data Refresh Service  

Chart Flare  

PictoBar   ProgBar  

Counter Calendar   Donut Chart image  

Financial Reporting Matrix by Profitbase 

Making financial statements with a proper layout has just become easier with the latest version of the Financial Reporting Matrix. 

Users are now able to specify which rows should be classified as cost-rows, which will make it easier to get the conditional formatting of variances correctly: 

Selecting a row, and ticking “is cost” will tag the row as cost. This can be used in conditional formatting to make sure that positive variances on expenses are a bad for the result, while a positive variance on an income row is good for the result. 

The new version also includes more flexibility in measuring placement and column subtotals. 

Measures can be placed either: 

• Default (below column headers) 
• Above column headers 

• Conditionally hide columns 
• + much more 

Highlighted new features:  

• Measure placement – In rows  
• Select Column Subtotals  
• New Format Pane design 
• Row Options  

Get the visual from AppSource and find more videos here ! 

Horizon Chart by Powerviz  

A Horizon Chart is an advanced visual, for time-series data, revealing trends and anomalies. It displays stacked data layers, allowing users to compare multiple categories while maintaining data clarity. Horizon Charts are particularly useful to monitor and analyze complex data over time, making this a valuable visual for data analysis and decision-making. 

Key Features:  

• Horizon Styles: Choose Natural, Linear, or Step with adjustable scaling. 
• Layer: Layer data by range or custom criteria. Display positive and negative values together or separately on top. 
• Reference Line : Highlight patterns with X-axis lines and labels. 
• Colors: Apply 30+ color palettes and use FX rules for dynamic coloring. 
• Ranking: Filter Top/Bottom N values, with “Others”. 
• Gridline: Add gridlines to the X and Y axis.  
• Custom Tooltip: Add highest, lowest, mean, and median points without additional DAX. 
• Themes: Save designs and share seamlessly with JSON files. 

Other features included are ranking, annotation, grid view, show condition, and accessibility support.  

Business Use Cases: Time-Series Data Comparison, Environmental Monitoring, Anomaly Detection 

🔗 Try Horizon Chart for FREE from AppSource  

📊 Check out all features of the visual: Demo file  

📃 Step-by-step instructions: Documentation  

💡 YouTube Video: Video Link  

📍 Learn more about visuals: https://powerviz.ai/  

✅ Follow Powerviz : https://lnkd.in/gN_9Sa6U  

Exciting news! Thanks to your valuable feedback, we’ve enhanced our Milestone Trend Analysis Chart even further. We’re thrilled to announce that you can now switch between horizontal and vertical orientations, catering to your preferred visualization style.

The Milestone Trend Analysis (MTA) Chart remains your go-to tool for swiftly identifying deadline trends, empowering you to take timely corrective actions. With this update, we aim to enhance deadline awareness among project participants and stakeholders alike. 

In our latest version, we seamlessly navigate between horizontal and vertical views within the familiar Power BI interface. No need to adapt to a new user interface – enjoy the same ease of use with added flexibility. Plus, it benefits from supported features like themes, interactive selection, and tooltips. 

What’s more, ours is the only Microsoft Certified Milestone Trend Analysis Chart for Power BI, ensuring reliability and compatibility with the platform. 

Ready to experience the enhanced Milestone Trend Analysis Chart? Download it from AppSource today and explore its capabilities with your own data – try for free!  

We welcome any questions or feedback at our website: https://visuals.novasilva.com/ . Try it out and elevate your project management insights now! 

Sunburst Chart by Powerviz  

Powerviz’s Sunburst Chart is an interactive tool for hierarchical data visualization. With this chart, you can easily visualize multiple columns in a hierarchy and uncover valuable insights. The concentric circle design helps in displaying part-to-whole relationships. 

• Arc Customization: Customize shapes and patterns. 
• Color Scheme: Accessible palettes with 30+ options. 
• Centre Circle: Design an inner circle with layers. Add text, measure, icons, and images. 
• Conditional Formatting: Easily identify outliers based on measure or category rules. 
• Labels: Smart data labels for readability. 
• Image Labels: Add an image as an outer label. 
• Interactivity: Zoom, drill down, cross-filtering, and tooltip features. 

Other features included are annotation, grid view, show condition, and accessibility support.  

Business Use Cases:   

• Sales and Marketing: Market share analysis and customer segmentation. 
• Finance : Department budgets and expenditures distribution. 
• Operations : Supply chain management. 
• Education : Course structure, curriculum creation. 
• Human Resources : Organization structure, employee demographics.

🔗 Try Sunburst Chart for FREE from AppSource  

Stacked Bar Chart with Line by JTA  

Clustered bar chart with the possibility to stack one of the bars  

Stacked Bar Chart with Line by JTA seamlessly merges the simplicity of a traditional bar chart with the versatility of a stacked bar, revolutionizing the way you showcase multiple datasets in a single, cohesive display. 

Unlocking a new dimension of insight, our visual features a dynamic line that provides a snapshot of data trends at a glance. Navigate through your data effortlessly with multiple configurations, gaining a swift and comprehensive understanding of your information. 

Tailor your visual experience with an array of functionalities and customization options, enabling you to effortlessly compare a primary metric with the performance of an entire set. The flexibility to customize the visual according to your unique preferences empowers you to harness the full potential of your data. 

Features of Stacked Bar Chart with Line:  

• Stack the second bar 
• Format the Axis and Gridlines 
• Add a legend 
• Format the colors and text 
• Add a line chart 
• Format the line 
• Add marks to the line 
• Format the labels for bars and line 

If you liked what you saw, you can try it for yourself and find more information here . Also, if you want to download it, you can find the visual package on the AppSource . 

We have added an exciting new feature to our Combo PRO, Combo Bar PRO, and Timeline PRO visuals – Legend field support . The Legend field makes it easy to visually split series values into smaller segments, without the need to use measures or create separate series. Simply add a column with category names that are adjacent to the series values, and the visual will do the following:  

• Display separate segments as a stack or cluster, showing how each segment contributed to the total Series value. 
• Create legend items for each segment to quickly show/hide them without filtering.  
• Apply custom fill colors to each segment.  
• Show each segment value in the tooltip 

Read more about the Legend field on our blog article  

Drill Down Combo PRO is made for creators who want to build visually stunning and user-friendly reports. Cross-chart filtering and intuitive drill down interactions make data exploration easy and fun for any user. Furthermore, you can choose between three chart types – columns, lines, or areas; and feature up to 25 different series in the same visual and configure each series independently.  

📊 Get Drill Down Combo PRO on AppSource  

🌐 Visit Drill Down Combo PRO product page  

Documentation | ZoomCharts Website | Follow ZoomCharts on LinkedIn  

We are thrilled to announce that Fabric Core REST APIs are now generally available! This marks a significant milestone in the evolution of Microsoft Fabric, a platform that has been meticulously designed to empower developers and businesses alike with a comprehensive suite of tools and services. 

The Core REST APIs are the backbone of Microsoft Fabric, providing the essential building blocks for a myriad of functionalities within the platform. They are designed to improve efficiency, reduce manual effort, increase accuracy, and lead to faster processing times. These APIs help with scale operations more easily and efficiently as the volume of work grows, automate repeatable processes with consistency, and enable integration with other systems and applications, providing a streamlined and efficient data pipeline. 

The Microsoft Fabric Core APIs encompasses a range of functionalities, including: 

• Workspace management: APIs to manage workspaces, including permissions.  
• Item management: APIs for creating, reading, updating, and deleting items, with partial support for data source discovery and granular permissions management planned for the near future. 
• Job and tenant management: APIs to manage jobs, tenants, and users within the platform. 

These APIs adhere to industry standards and best practices, ensuring a unified developer experience that is both coherent and easy to use. 

For developers looking to dive into the details of the Microsoft Fabric Core APIs, comprehensive documentation is available. This includes guidelines on API usage, examples, and articles managed in a centralized repository for ease of access and discoverability. The documentation is continuously updated to reflect the latest features and improvements, ensuring that developers have the most current information at their fingertips. See Microsoft Fabric REST API documentation  

We’re excited to share an important update we made to the Fabric Admin APIs. This enhancement is designed to simplify your automation experience. Now, you can manage both Power BI and the new Fabric items (previously referred to as artifacts) using the same set of APIs. Before this enhancement, you had to navigate using two different APIs—one for Power BI items and another for new Fabric items. That’s no longer the case. 

The APIs we’ve updated include GetItem , ListItems , GetItemAccessDetails , and GetAccessEntities . These enhancements mean you can now query and manage all your items through a single API call, regardless of whether they’re Fabric types or Power BI types. We hope this update makes your work more straightforward and helps you accomplish your tasks more efficiently. 

We’re thrilled to announce the public preview of the Microsoft Fabric workload development kit. This feature now extends to additional workloads and offers a robust developer toolkit for designing, developing, and interoperating with Microsoft Fabric using frontend SDKs and backend REST APIs. Introducing the Microsoft Fabric Workload Development Kit . 

The Microsoft Fabric platform now provides a mechanism for ISVs and developers to integrate their new and existing applications natively into Fabric’s workload hub. This integration provides the ability to add net new capabilities to Fabric in a consistent experience without leaving their Fabric workspace, thereby accelerating data driven outcomes from Microsoft Fabric. 

By downloading and leveraging the development kit , ISVs and software developers can build and scale existing and new applications on Microsoft Fabric and offer them via the Azure Marketplace without the need to ever leave the Fabric environment. 

The development kit provides a comprehensive guide and sample code for creating custom item types that can be added to the Fabric workspace. These item types can leverage the Fabric frontend SDKs and backend REST APIs to interact with other Fabric capabilities, such as data ingestion, transformation, orchestration, visualization, and collaboration. You can also embed your own data application into the Fabric item editor using the Fabric native experience components, such as the header, toolbar, navigation pane, and status bar. This way, you can offer consistent and seamless user experience across different Fabric workloads. 

This is a call to action for ISVs, software developers, and system integrators. Let’s leverage this opportunity to create more integrated and seamless experiences for our users. 

We’re excited about this journey and look forward to seeing the innovative workloads from our developer community. 

We are proud to announce the public preview of external data sharing. Sharing data across organizations has become a standard part of day-to-day business for many of our customers. External data sharing, built on top of OneLake shortcuts, enables seamless, in-place sharing of data, allowing you to maintain a single copy of data even when sharing data across tenant boundaries. Whether you’re sharing data with customers, manufacturers, suppliers, consultants, or partners; the applications are endless. 

How external data sharing works  

Sharing data across tenants is as simple as any other share operation in Fabric. To share data, navigate to the item to be shared, click on the context menu, and then click on External data share . Select the folder or table you want to share and click Save and continue . Enter the email address and an optional message and then click Send . 

The data consumer will receive an email containing a share link. They can click on the link to accept the share and access the data within their own tenant. 

Click here for more details about external data sharing . 

Following the release of OneLake data access roles in public preview, the OneLake team is excited to announce the availability of APIs for managing data access roles. These APIs can be used to programmatically manage granular data access for your lakehouses. Manage all aspects of role management such as creating new roles, editing existing ones, or changing memberships in a programmatic way.  

Do you have data stored on-premises or behind a firewall that you want to access and analyze with Microsoft Fabric? With OneLake shortcuts, you can bring on-premises or network-restricted data into OneLake, without any data movement or duplication. Simply install the Fabric on-premises data gateway and create a shortcut to your S3 compatible, Amazon S3, or Google Cloud Storage data source. Then use any of Fabric’s powerful analytics engines and OneLake open APIs to explore, transform, and visualize your data in the cloud. 

Try it out today and unlock the full potential of your data with OneLake shortcuts! 

Data Warehouse 

We are excited to announce Copilot for Data Warehouse in public preview! Copilot for Data Warehouse is an AI assistant that helps developers generate insights through T-SQL exploratory analysis. Copilot is contextualized your warehouse’s schema. With this feature, data engineers and data analysts can use Copilot to: 

• Generate T-SQL queries for data analysis.  
• Explain and add in-line code comments for existing T-SQL queries. 
• Fix broken T-SQL code. 
• Receive answers regarding general data warehousing tasks and operations. 

There are 3 areas where Copilot is surfaced in the Data Warehouse SQL Query Editor: 

• Code completions when writing a T-SQL query. 
• Chat panel to interact with the Copilot in natural language. 
• Quick action buttons to fix and explain T-SQL queries. 

Learn more about Copilot for Data Warehouse: aka.ms/data-warehouse-copilot-docs. Copilot for Data Warehouse is currently only available in the Warehouse. Copilot in the SQL analytics endpoint is coming soon. 

Unlocking Insights through Time: Time travel in Data warehouse (public preview)

As data volumes continue to grow in today’s rapidly evolving world of Artificial Intelligence, it is crucial to reflect on historical data. It empowers businesses to derive valuable insights that aid in making well-informed decisions for the future. Preserving multiple historical data versions not only incurred significant costs but also presented challenges in upholding data integrity, resulting in a notable impact on query performance. So, we are thrilled to announce the ability to query the historical data through time travel at the T-SQL statement level which helps unlock the evolution of data over time. 

The Fabric warehouse retains historical versions of tables for seven calendar days. This retention allows for querying the tables as if they existed at any point within the retention timeframe. Time travel clause can be included in any top level SELECT statement. For complex queries that involve multiple tables, joins, stored procedures, or views, the timestamp is applied just once for the entire query instead of specifying the same timestamp for each table within the same query. This ensures the entire query is executed with reference to the specified timestamp, maintaining the data’s uniformity and integrity throughout the query execution. 

From historical trend analysis and forecasting to compliance management, stable reporting and real-time decision support, the benefits of time travel extend across multiple business operations. Embrace the capability of time travel to navigate the data-driven landscape and gain a competitive edge in today’s fast-paced world of Artificial Intelligence. 

We are excited to announce not one but two new enhancements to the Copy Into feature for Fabric Warehouse: Copy Into with Entra ID Authentication and Copy Into for Firewall-Enabled Storage!

Entra ID Authentication  

When authenticating storage accounts in your environment, the executing user’s Entra ID will now be used by default. This ensures that you can leverage A ccess C ontrol L ists and R ole – B ased a ccess c ontrol to authenticate to your storage accounts when using Copy Into. Currently, only organizational accounts are supported.  

How to Use Entra ID Authentication  

• Ensure your Entra ID organizational account has access to the underlying storage and can execute the Copy Into statement on your Fabric Warehouse.  
• Run your Copy Into statement without specifying any credentials; the Entra ID organizational account will be used as the default authentication mechanism.  

Copy into firewall-enabled storage

The Copy Into for firewall-enabled storage leverages the trusted workspace access functionality ( Trusted workspace access in Microsoft Fabric (preview) – Microsoft Fabric | Microsoft Learn ) to establish a secure and seamless connection between Fabric and your storage accounts. Secure access can be enabled for both blob and ADLS Gen2 storage accounts. Secure access with Copy Into is available for warehouses in workspaces with Fabric Capacities (F64 or higher).  

To learn more about Copy into , please refer to COPY INTO (Transact-SQL) – Azure Synapse Analytics and Microsoft Fabric | Microsoft Learn  

We are excited to announce the launch of our new feature, Just in Time Database Attachment, which will significantly enhance your first experience, such as when connecting to the Datawarehouse or SQL endpoint or simply opening an item. These actions trigger the workspace resource assignment process, where, among other actions, we attach all necessary metadata of your items, Data warehouses and SQL endpoints, which can be a long process, particularly for workspaces that have a high number of items.  

This feature is designed to attach your desired database during the activation process of your workspace, allowing you to execute queries immediately and avoid unnecessary delays. However, all other databases will be attached asynchronously in the background while you are able to execute queries, ensuring a smooth and efficient experience. 

Data Engineering 

We are advancing Fabric Runtime 1.3 from an Experimental Public Preview to a full Public Preview. Our Apache Spark-based big data execution engine, optimized for both data engineering and science workflows, has been updated and fully integrated into the Fabric platform. 

The enhancements in Fabric Runtime 1.3 include the incorporation of Delta Lake 3.1, compatibility with Python 3.11, support for Starter Pools, integration with Environment and library management capabilities. Additionally, Fabric Runtime now enriches the data science experience by supporting the R language and integrating Copilot. 

We are pleased to share that the Native Execution Engine for Fabric Runtime 1.2 is currently available in public preview. The Native Execution Engine can greatly enhance the performance for your Spark jobs and queries. The engine has been rewritten in C++ and operates in columnar mode and uses vectorized processing. The Native Execution Engine offers superior query performance – encompassing data processing, ETL, data science, and interactive queries – all directly on your data lake. Overall, Fabric Spark delivers a 4x speed-up on the sum of execution time of all 99 queries in the TPC-DS 1TB benchmark when compared against Apache Spark.  This engine is fully compatible with Apache Spark™ APIs (including Spark SQL API). 

It is seamless to use with no code changes – activate it and go. Enable it in your environment for your notebooks and your SJDs. 

This feature is in the public preview, at this stage of the preview, there is no additional cost associated with using it. 

We are excited to announce the Spark Monitoring Run Series Analysis features, which allow you to analyze the run duration trend and performance comparison for Pipeline Spark activity recurring run instances and repetitive Spark run activities from the same Notebook or Spark Job Definition.   

• Run Series Comparison: Users can compare the duration of a Notebook run with that of previous runs and evaluate the input and output data to understand the reasons behind prolonged run durations.  
• Outlier Detection and Analysis: The system can detect outliers in the run series and analyze them to pinpoint potential contributing factors. 
• Detailed Run Instance Analysis: Clicking on a specific run instance provides detailed information on time distribution, which can be used to identify performance enhancement opportunities. 
• Configuration Insights : Users can view the Spark configuration used for each run, including auto-tuned configurations for Spark SQL queries in auto-tune enabled Notebook runs. 

You can access the new feature from the item’s recent runs panel and Spark application monitoring page. 

We are excited to announce that Notebook now supports the ability to tag others in comments, just like the familiar functionality of using Office products!   

When you select a section of code in a cell, you can add a comment with your insights and tag one or more teammates to collaborate or brainstorm on the specifics. This intuitive enhancement is designed to amplify collaboration in your daily development work. 

Moreover, you can easily configure the permissions when tagging someone who doesn’t have the permission, to make sure your code asset is well managed. 

We are thrilled to unveil a significant enhancement to the Fabric notebook ribbon, designed to elevate your data science and engineering workflows. 

In the new version, you will find the new Session connect control on the Home tab, and now you can start a standard session without needing to run a code cell. 

You can also easily spin up a High concurrency session and share the session across multiple notebooks to improve the compute resource utilization. And you can easily attach/leave a high concurrency session with a single click. 

The “ View session information ” can navigate you to the session information dialog, where you can find a lot of useful detailed information, as well as configure the session timeout. The diagnostics info is essentially helpful when you need support for notebook issues. 

Now you can easily access the powerful “ Data Wrangler ” on Home tab with the new ribbon! You can explore your data with the fancy low-code experience of data wrangler, and the pandas DataFrames and Spark DataFrames are all supported.   

We recently made some changes to the Fabric notebook metadata to ensure compliance and consistency: 

Notebook file content: 

• The keyword “trident” has been replaced with “dependencies” in the notebook content. This adjustment ensures consistency and compliance. 
• Notebook Git format: 
• The preface of the notebook has been modified from “# Synapse Analytics notebook source” to “# Fabric notebook source”. 
• Additionally, the keyword “synapse” has been updated to “dependencies” in the Git repo. 

The above changes will be marked as ‘uncommitted’ for one time if your workspace is connected to Git. No action is needed in terms of these changes , and there won’t be any breaking scenario within the Fabric platform . If you have any further updates or questions, feel free to share with us. 

We are thrilled to announce that the environment is now a generally available item in Microsoft Fabric. During this GA timeframe, we have shipped a few new features of Environment. 

• Git support  

The environment is now Git supported. You can check-in the environment into your Git repo and manipulate the environment locally with its YAML representations and custom library files. After updating the changes from local to Fabric portal, you can publish them by manual action or through REST API. 

• Deployment pipeline  

Deploying environments from one workspace to another is supported.  Now, you can deploy the code items and their dependent environments together from development to test and even production. 

With the REST APIs, you can have the code-first experience with the same abilities through Fabric portal. We provide a set of powerful APIs to ensure you the efficiency in managing your environment. You can create new environments, update libraries and Spark compute, publish the changes, delete an environment, attach the environment to a notebook, etc., all actions can be done locally in the tools of your choice. The article – Best practice of managing environments with REST API could help you get started with several real-world scenarios.  

• Resources folder   

Resources folder enables managing small resources in the development cycle. The files uploaded in the environment can be accessed from notebooks once they’re attached to the same environment. The manipulation of the files and folders of resources happens in real-time. It could be super powerful, especially when you are collaborating with others. 

Sharing your environment with others is also available. We provide several sharing options. By default, the view permission is shared. If you want the recipient to have access to view and use the contents of the environment, sharing without permission customization is the best option. Furthermore, you can grant editing permission to allow recipients to update this environment or grant share permission to allow recipients to reshare this environment with their existing permissions. 

We are excited to announce the REST api support for Fabric Data Engineering/Science workspace settings.  Data Engineering/Science settings allows users to create/manage their Spark compute, select the default runtime/default environment, enable or disable high concurrency mode or ML autologging.  

Now with the REST api support for the Data Engineering/Science settings, you would be able to  

• Choose the default pool for a Fabric Workspace 
• Configure the max nodes for Starter pools 
• Create/Update/Delete the existing Custom Pools, Autoscale and Dynamic allocation properties  
• Choose Workspace Default Runtime and Environment  
• Select a default runtime 
• Select the default environment for the Fabric workspace  
• Enable or Disable High Concurrency Mode 
• Enable or Disable ML Auto logging.  

Learn more about the Workspace Spark Settings API in our API documentation Workspace Settings – REST API (Spark) | Microsoft Learn  

We are excited to give you a sneak peek at the preview of User Data Functions in Microsoft Fabric. User Data Functions gives developers and data engineers the ability to easily write and run applications that integrate with resources in the Fabric Platform. Data engineering often presents challenges with data quality or complex data analytics processing in data pipelines, and using ETL tools may present limited flexibility and ability to customize to your needs. This is where User data functions can be used to run data transformation tasks and perform complex business logic by connecting to your data sources and other workloads in Fabric.  

During preview, you will be able to use the following features:  

• Use the Fabric portal to create new User Data Functions, view and test them.  
• Write your functions using C#.   
• Use the Visual Studio Code extension to create and edit your functions.  
• Connect to the following Fabric-native data sources: Data Warehouse, Lakehouse and Mirrored Databases.   

You can now create a fully managed GraphQL API in Fabric to interact with your data in a simple, flexible, and powerful way. We’re excited to announce the public preview of API for GraphQL, a data access layer that allows us to query multiple data sources quickly and efficiently in Fabric by leveraging a widely adopted and familiar API technology that returns more data with less client requests.  With the new API for GraphQL in Fabric, data engineers and scientists can create data APIs to connect to different data sources, use the APIs in their workflows, or share the API endpoints with app development teams to speed up and streamline data analytics application development in your business. 

You can get started with the API for GraphQL in Fabric by creating an API, attaching a supported data source, then selecting specific data sets you want to expose through the API. Fabric builds the GraphQL schema automatically based on your data, you can test and prototype queries directly in our graphical in-browser GraphQL development environment (API editor), and applications are ready to connect in minutes. 

Currently, the following supported data sources can be exposed through the Fabric API for GraphQL: 

• Microsoft Fabric Data Warehouse 
• Microsoft Fabric Lakehouse via SQL Analytics Endpoint 
• Microsoft Fabric Mirrored Databases via SQL Analytics Endpoint 

Click here to learn more about how to get started. 

Data Science 

As you may know, Copilot in Microsoft Fabric requires your tenant administrator to enable the feature from the admin portal. Starting May 20th, 2024, Copilot in Microsoft Fabric will be enabled by default for all tenants. This update is part of our continuous efforts to enhance user experience and productivity within Microsoft Fabric. This new default activation means that AI features like Copilot will be automatically enabled for tenants who have not yet enabled the setting.  

We are introducing a new capability to enable Copilot on Capacity level in Fabric. A new option is being introduced in the tenant admin portal, to delegate the enablement of AI and Copilot features to Capacity administrators.  This AI and Copilot setting will be automatically delegated to capacity administrators and tenant administrators won’t be able to turn off the delegation.   

We also have a cross-geo setting for customers who want to use Copilot and AI features while their capacity is in a different geographic region than the EU data boundary or the US. By default, the cross-geo setting will stay off and will not be delegated to capacity administrators automatically.  Tenant administrators can choose whether to delegate this to capacity administrators or not. 

Figure 1.  Copilot in Microsoft Fabric will be auto enabled and auto delegated to capacity administrators. 

Capacity administrators will see the “Copilot and Azure OpenAI Service (preview)” settings under Capacity settings/ Fabric Capacity / <Capacity name> / Delegated tenant settings. By default, the capacity setting will inherit tenant level settings. Capacity administrators can decide whether to override the tenant administrator’s selection. This means that even if Copilot is not enabled on a tenant level, a capacity administrator can choose to enable Copilot for their capacity. With this level of control, we make it easier to control which Fabric workspaces can utilize AI features like Copilot in Microsoft Fabric. 

To enhance privacy and trust, we’ve updated our approach to abuse monitoring: previously, we retained data from Copilot in Fabric, including prompt inputs and outputs, for up to 30 days to check for misuse. Following customer feedback, we’ve eliminated this 30-day retention. Now, we no longer store prompt related data, demonstrating our unwavering commitment to your privacy and security. We value your input and take your concerns seriously. 

Real-Time Intelligence 

This month includes the announcement of Real-Time Intelligence, the next evolution of Real-Time Analytics and Data Activator. With Real-Time Intelligence, Fabric extends to the world of streaming and high granularity data, enabling all users in your organization to collect, analyze and act on this data in a timeline manner making faster and more informed business decisions. Read the full announcement from Build 2024. 

Real-Time Intelligence includes a wide range of capabilities across ingestion, processing, analysis, transformation, visualization and taking action. All of this is supported by the Real-Time hub, the central place to discover and manage streaming data and start all related tasks.  

Read on for more information on each capability and stay tuned for a series of blogs describing the features in more detail. All features are in Public Preview unless otherwise specified. Feedback on any of the features can be submitted at https://aka.ms/rtiidea    

Ingest & Process  

• Introducing the Real-Time hub 
• Get Events with new sources of streaming and event data 
• Source from Real-Time Hub in Enhanced Eventstream  
• Use Real-Time hub to Get Data in KQL Database in Eventhouse 
• Get data from Real-Time Hub within Reflexes 
• Eventstream Edit and Live modes 
• Default and derived streams 
• Route data streams based on content 

Analyze & Transform  

• Eventhouse GA 
• Eventhouse OneLake availability GA 
• Create a database shortcut to another KQL Database 
• Support for AI Anomaly Detector  
• Copilot for Real-Time Intelligence 
• Tenant-level private endpoints for Eventhouse 

Visualize & Act  

• Visualize data with Real-Time Dashboards  
• New experience for data exploration 
• Create triggers from Real-Time Hub 
• Set alert on Real-time Dashboards 
• Taking action through Fabric Items 

Ingest & Process 

Real-Time hub is the single place for all data-in-motion across your entire organization. Several key features are offered in Real-Time hub: 

1. Single place for data-in-motion for the entire organization  

Real-Time hub enables users to easily discover, ingest, manage, and consume data-in-motion from a wide variety of sources. It lists all the streams and KQL tables that customers can directly act on. 

2. Real-Time hub is never empty  

All data streams in Fabric automatically show up in the hub. Also, users can subscribe to events in Fabric gaining insights into the health and performance of their data ecosystem. 

3. Numerous connectors to simplify data ingestion from anywhere to Real-Time hub  

Real-Time hub makes it easy for you to ingest data into Fabric from a wide variety of sources like AWS Kinesis, Kafka clusters, Microsoft streaming sources, sample data and Fabric events using the Get Events experience.  

There are 3 tabs in the hub:  

• Data streams : This tab contains all streams that are actively running in Fabric that user has access to. This includes all streams from Eventstreams and all tables from KQL Databases. 
• Microsoft sources : This tab contains Microsoft sources (that user has access to) and can be connected to Fabric. 
• Fabric events : Fabric now has event-driven capabilities to support real-time notifications and data processing. Users can monitor and react to events including Fabric Workspace Item events and Azure Blob Storage events. These events can be used to trigger other actions or workflows, such as invoking a data pipeline or sending a notification via email. Users can also send these events to other destinations via Event Streams. 

Learn More  

You can now connect to data from both inside and outside of Fabric in a mere few steps.  Whether data is coming from new or existing sources, streams, or available events, the Get Events experience allows users to connect to a wide range of sources directly from Real-Time hub, Eventstreams, Eventhouse and Data Activator.  

This enhanced capability allows you to easily connect external data streams into Fabric with out-of-box experience, giving you more options and helping you to get real-time insights from various sources. This includes Camel Kafka connectors powered by Kafka connect to access popular data platforms, as well as the Debezium connectors for fetching the Change Data Capture (CDC) streams. 

Using Get Events, bring streaming data from Microsoft sources directly into Fabric with a first-class experience.  Connectivity to notification sources and discrete events is also included, this enables access to notification events from Azure and other clouds solutions including AWS and GCP.  The full set of sources which are currently supported are: 

• Microsoft sources : Azure Event Hubs, Azure IoT hub 
• External sources : Google Cloud Pub/Sub, Amazon Kinesis Data Streams, Confluent Cloud Kafka 
• Change data capture databases : Azure SQL DB (CDC), PostgreSQL DB (CDC), Azure Cosmos DB (CDC), MySQL DB (CDC)  
• Fabric events : Fabric Workspace Item events, Azure Blob Storage events  

Learn More   

With enhanced Eventstream, you can now stream data not only from Microsoft sources but also from other platforms like Google Cloud, Amazon Kinesis, Database change data capture streams, etc. using our new messaging connectors. The new Eventstream also lets you acquire and route real-time data not only from stream sources but also from discrete event sources, such as: Azure Blob Storage events, Fabric Workspace Item events. 

To use these new sources in Eventstream, simply create an eventstream with choosing “Enhanced Capabilities (preview)”. 

You will see the new Eventstream homepage that gives you some choices to begin with. By clicking on the “Add external source”, you will find these sources in the Get events wizard that helps you to set up the source in a few steps. After you add the source to your eventstream, you can publish it to stream the data into your eventstream.  

Using Eventstream with discrete sources to turn events into streams for more analysis. You can send the streams to different Fabric data destinations, like Lakehouse and KQL Database. After the events are converted, a default stream will appear in Real-Time Hub. To turn them, click Edit on ribbon, select “Stream events” on the source node, and publish your eventstream. 

To transform the stream data or route it to different Fabric destinations based on its content, you can click Edit in ribbon and enter the Edit mode. There you can add event processing operators and destinations. 

With Real-Time hub embedded in KQL Database experience, each user in the tenant can view and add streams which they have access to and directly ingest it to a KQL Database table in Eventhouse.  

This integration provides each user in the tenant with the ability to access and view data streams they are permitted to. They can now directly ingest these streams into a KQL Database table in Eventhouse. This simplifies the data discovery and ingestion process by allowing users to directly interact with the streams. Users can filter data based on the Owner, Parent and Location and provides additional information such as Endorsement and Sensitivity. 

You can access this by clicking on the Get Data button from the Database ribbon in Eventhouse. 

This will open the Get Data wizard with Real-Time hub embedded. 

You can use events from Real-Time hub directly in reflex items as well. From within the main reflex UI, click ‘Get data’ in the toolbar: 

This will open a wizard that allows you to connect to new event sources or browse Real-Time Hub to use existing streams or system events. 

Search new stream sources to connect to or select existing streams and tables to be ingested directly by Reflex. 

You then have access to the full reflex modeling experience to build properties and triggers over any events from Real-Time hub.  

Eventstream offers two distinct modes, Edit and Live, to provide flexibility and control over the development process of your eventstream. If you create a new Eventstream with Enhanced Capabilities enabled, you can modify it in an Edit mode. Here, you can design stream processing operations for your data streams using a no-code editor. Once you complete the editing, you can publish your Eventstream and visualize how it starts streaming and processing data in Live mode .   

In Edit mode, you can:   

• Make changes to an Eventstream without implementing them until you publish the Eventstream. This gives you full control over the development process.  
• Avoid test data being streamed to your Eventstream. This mode is designed to provide a secure environment for testing without affecting your actual data streams. 

For Live mode, you can :  

• Visualize how your Eventstream streams, transforms, and routes your data streams to various destinations after publishing the changes.  
• Pause the flow of data on selected sources and destinations, providing you with more control over your data streams being streamed into your Eventstream.  

When you create a new Eventstream with Enhanced Capabilities enabled, you can now create and manage multiple data streams within Eventstream, which can then be displayed in the Real-Time hub for others to consume and perform further analysis.  

There are two types of streams:   

• Default stream : Automatically generated when a streaming source is added to Eventstream. Default stream captures raw event data directly from the source, ready for transformation or analysis.  
• Derived stream : A specialized stream that users can create as a destination within Eventstream. Derived stream can be created after a series of operations such as filtering and aggregating, and then it’s ready for further consumption or analysis by other users in the organization through the Real-Time Hub.  

The following example shows that when creating a new Eventstream a default stream alex-es1-stream is automatically generated. Subsequently, a derived stream dstream1 is added after an Aggregate operation within the Eventstream. Both default and derived streams can be found in the Real-Time hub.  

Customers can now perform stream operations directly within Eventstream’s Edit mode, instead of embedding in a destination. This enhancement allows you to design stream processing logics and route data streams in the top-level canvas. Custom processing and routing can be applied to individual destinations using built-in operations, allowing for routing to distinct destinations within the Eventstream based on different stream content. 

These operations include:  

• Aggregate : Perform calculations such as SUM, AVG, MIN, and MAX on a column of values and return a single result. 
• Expand : Expand array values and create new rows for each element within the array.  
• Filter : Select or filter specific rows from the data stream based on a condition. 
• Group by : Aggregate event data within a certain time window, with the option to group one or more columns.  
• Manage Fields : Customize your data streams by adding, removing, or changing data type of a column.  
• Union : Merge two or more data streams with shared fields (same name and data type) into a unified data stream.  

Analyze & Transform 

Eventhouse, a cutting-edge database workspace meticulously crafted to manage and store event-based data, is now officially available for general use. Optimized for high granularity, velocity, and low latency streaming data, it incorporates indexing and partitioning for structured, semi-structured, and free text data. With Eventhouse, users can perform high-performance analysis of big data and real-time data querying, processing billions of events within seconds. The platform allows users to organize data into compartments (databases) within one logical item, facilitating efficient data management.  

Additionally, Eventhouse enables the sharing of compute and cache resources across databases, maximizing resource utilization. It also supports high-performance queries across databases and allows users to apply common policies seamlessly. Eventhouse offers content-based routing to multiple databases, full view lineage, and high granularity permission control, ensuring data security and compliance. Moreover, it provides a simple migration path from Azure Synapse Data Explorer and Azure Data Explorer, making adoption seamless for existing users. 

Engineered to handle data in motion, Eventhouse seamlessly integrates indexing and partitioning into its storing process, accommodating various data formats. This sophisticated design empowers high-performance analysis with minimal latency, facilitating lightning-fast ingestion and querying within seconds. Eventhouse is purpose-built to deliver exceptional performance and efficiency for managing event-based data across diverse applications and industries. Its intuitive features and seamless integration with existing Azure services make it an ideal choice for organizations looking to leverage real-time analytics for actionable insights. Whether it’s analyzing telemetry and log data, time series and IoT data, or financial records, Eventhouse provides the tools and capabilities needed to unlock the full potential of event-based data. 

We’re excited to announce that OneLake availability of Eventhouse in Delta Lake format is Generally Available. 

Delta Lake  is the unified data lake table format chosen to achieve seamless data access across all compute engines in Microsoft Fabric. 

The data streamed into Eventhouse is stored in an optimized columnar storage format with full text indexing and supports complex analytical queries at low latency on structured, semi-structured, and free text data. 

Enabling data availability of Eventhouse in OneLake means that customers can enjoy the best of both worlds: they can query the data with high performance and low latency in their  Eventhouse and query the same data in Delta Lake format via any other Fabric engines such as Power BI Direct Lake mode, Warehouse, Lakehouse, Notebooks, and more. 

To learn more, please visit https://learn.microsoft.com/en-gb/fabric/real-time-analytics/one-logical-copy 

A database shortcut in Eventhouse is an embedded reference to a source database. The source database can be one of the following: 

• (Now Available) A KQL Database in Real-Time Intelligence  
• An Azure Data Explorer database  

The behavior exhibited by the database shortcut is similar to that of a follower database  

The owner of the source database, the data provider, shares the database with the creator of the shortcut in Real-Time Intelligence, the data consumer. The owner and the creator can be the same person. The database shortcut is attached in read-only mode, making it possible to view and run queries on the data that was ingested into the source KQL Database without ingesting it.  

This helps with data sharing scenarios where you can share data in-place either within teams, or even with external customers.  

AI Anomaly Detector is an Azure service for high quality detection of multivariate and univariate anomalies in time series. While the standalone version is being retired October 2026, Microsoft open sourced the anomaly detection core algorithms and they are now supported in Microsoft Fabric. Users can leverage these capabilities in Data Science and Real-Time Intelligence workload. AI Anomaly Detector models can be trained in Spark Python notebooks in Data Science workload, while real time scoring can be done by KQL with inline Python in Real-Time Intelligence. 

We are excited to announce the Public Preview of Copilot for Real-Time Intelligence. This initial version includes a new capability that translates your natural language questions about your data to KQL queries that you can run and get insights.  

Your starting point is a KQL Queryset, that is connected to a KQL Database, or to a standalone Kusto database:  

Simply type the natural language question about what you want to accomplish, and Copilot will automatically translate it to a KQL query you can execute. This is extremely powerful for users who may be less familiar with writing KQL queries but still want to get the most from their time-series data stored in Eventhouse. 

Stay tuned for more capabilities from Copilot for Real-Time Intelligence!   

Customers can increase their network security by limiting access to Eventhouse at a tenant-level, from one or more virtual networks (VNets) via private links. This will prevent unauthorized access from public networks and only permit data plane operations from specific VNets.  

Visualize & Act 

Real-Time Dashboards have a user-friendly interface, allowing users to quickly explore and analyze their data without the need for extensive technical knowledge. They offer a high refresh frequency, support a range of customization options, and are designed to handle big data.  

The following visual types are supported, and can be customized with the dashboard’s user-friendly interface: 

You can also define conditional formatting rules to format the visual data points by their values using colors, tags, and icons. Conditional formatting can be applied to a specific set of cells in a predetermined column or to entire rows, and lets you easily identify interesting data points. 

Beyond the support visual, Real-Time Dashboards provide several capabilities to allow you to interact with your data by performing slice and dice operations for deeper analysis and gaining different viewpoints. 

• Parameters are used as building blocks for dashboard filters and can be added to queries to filter the data presented by visuals. Parameters can be used to slice and dice dashboard visuals either directly by selecting parameter values in the filter bar or by using cross-filters. 
• Cross filters allow you to select a value in one visual and filter all other visuals on that dashboard based on the selected data point. 
• Drillthrough capability allows you to select a value in a visual and use it to filter the visuals in a target page in the same dashboard. When the target page opens, the value is pushed to the relevant filters.    

Real-Time Dashboards can be shared broadly and allow multiple stakeholders to view dynamic, real time, fresh data while easily interacting with it to gain desired insights. 

Directly from a real-time dashboard, users can refine their exploration using a user-friendly, form-like interface. This intuitive and dynamic experience is tailored for insights explorers craving insights based on real-time data. Add filters, create aggregations, and switch visualization types without writing queries to easily uncover insights.  

With this new feature, insights explorers are no longer bound by the limitations of pre-defined dashboards. As independent explorers, they have the freedom for ad-hoc exploration, leveraging existing tiles to kickstart their journey. Moreover, they can selectively remove query segments, and expand their view of the data landscape.  

Dive deep, extract meaningful insights, and chart actionable paths forward, all with ease and efficiency, and without having to write complex KQL queries.  

Data Activator allows you to monitor streams of data for various conditions and set up actions to be taken in response. These triggers are available directly within the Real-Time hub and in other workloads in Fabric. When the condition is detected, an action will automatically be kicked off such as sending alerts via email or Teams or starting jobs in Fabric items.  

When you browse the Real-Time Hub, you’ll see options to set triggers in the detail pages for streams. 

Selecting this will open a side panel where you can configure the events you want to monitor, the conditions you want to look for in the events, and the action you want to take while in the Real-Time hub experience. 

Completing this pane creates a new reflex item with a trigger that monitors the selected events and condition for you. Reflexes need to be created in a workspace supported by a Fabric or Power BI Premium capacity – this can be a trial capacity so you can get started with it today! 

Data Activator has been able to monitor Power BI report data since it was launched, and we now support monitoring of Real-Time Dashboard visuals in the same way.

From real-time dashboard tiles you can click the ellipsis (…) button and select “Set alert”

This opens the embedded trigger pane, where you can specify what conditions, you are looking for. You can choose whether to send email or Teams messages as the alert when these conditions are met.

When creating a new reflex trigger, from Real-time Hub or within the reflex item itself, you’ll notice a new ‘Run a Fabric item’ option in the Action section. This will create a trigger that starts a new Fabric job whenever its condition is met, kicking off a pipeline or notebook computation in response to Fabric events. A common scenario would be monitoring Azure Blob storage events via Real-Time Hub, and running data pipeline jobs when Blog Created events are detected. 

This capability is extremely powerful and moves Fabric from a scheduled driven platform to an event driven platform.  

Pipelines, spark jobs, and notebooks are just the first Fabric items we’ll support here, and we’re keen to hear your feedback to help prioritize what else we support. Please leave ideas and votes on https://aka.ms/rtiidea and let us know! 

Real-Time Intelligence, along with the Real-Time hub, revolutionizes what’s possible with real-time streaming and event data within Microsoft Fabric.  

Learn more and try it today https://aka.ms/realtimeintelligence   

Data Factory 

Dataflow gen2 .

We are thrilled to announce that the Power Query SDK is now generally available in Visual Studio Code! This marks a significant milestone in our commitment to providing developers with powerful tools to enhance data connectivity and transformation. 

The Power Query SDK is a set of tools that allow you as the developer to create new connectors for Power Query experiences available in products such as Power BI Desktop, Semantic Models, Power BI Datamarts, Power BI Dataflows, Fabric Dataflow Gen2 and more. 

This new SDK has been in public preview since November of 2022, and we’ve been hard at work improving this experience which goes beyond what the previous Power Query SDK in Visual Studio had to offer.  

The latest of these biggest improvements was the introduction of the Test Framework in March of 2024 that solidifies the developer experience that you can have within Visual Studio Code and the Power Query SDK for creating a Power Query connector. 

The Power Query SDK extension for Visual Studio will be deprecated by June 30, 2024, so we encourage you to give this new Power Query SDK in Visual Studio Code today if you haven’t.  

To get started with the Power Query SDK in Visual Studio Code, simply install it from the Visual Studio Code Marketplace . Our comprehensive documentation and tutorials are available to help you harness the full potential of your data. 

Join our vibrant community of developers to share insights, ask questions, and collaborate on exciting projects. Our dedicated support team is always ready to assist you with any queries. 

We look forward to seeing the innovative solutions you’ll create with the Power Query SDK in Visual Studio Code. Happy coding! 

Introducing a convenient enhancement to the Dataflows Gen2 Refresh History experience! Now, alongside the familiar “X” button in the Refresh History screen, you’ll find a shiny new Refresh Button . This small but mighty addition empowers users to refresh the status of their dataflow refresh history status without the hassle of exiting the refresh history and reopening it. Simply click the Refresh Button , and voilà! Your dataflow’s refresh history status screen is updated, keeping you in the loop with minimal effort. Say goodbye to unnecessary clicks and hello to streamlined monitoring! 

• [New] OneStream : The OneStream Power Query Connector enables you to seamlessly connect Data Factory to your OneStream applications by simply logging in with your OneStream credentials. The connector uses your OneStream security, allowing you to access only the data you have based on your permissions within the OneStream application. Use the connector to pull cube and relational data along with metadata members, including all their properties. Visit OneStream Power BI Connector to learn more. Find this connector in the other category. 

Data workflows  

We are excited to announce the preview of ‘Data workflows’, a new feature within the Data Factory that revolutionizes the way you build and manage your code-based data pipelines. Powered by Apache Airflow, Data workflows offer seamless authoring, scheduling, and monitoring experience for Python-based data processes defined as Directed Acyclic Graphs (DAGs). This feature brings a SaaS-like experience to running DAGs in a fully managed Apache Airflow environment, with support for autoscaling , auto-pause , and rapid cluster resumption to enhance cost-efficiency and performance.  

It also includes native cloud-based authoring capabilities and comprehensive support for Apache Airflow plugins and libraries. 

To begin using this feature: 

• Access the Microsoft Fabric Admin Portal. 
• Navigate to Tenant Settings. 

Under Microsoft Fabric options, locate and expand the ‘Users can create and use Data workflows (preview)’ section. Note: This action is necessary only during the preview phase of Data workflows. 

2. Create a new Data workflow within an existing or new workspace. 

3. Add a new Directed Acyclic Graph (DAG) file via the user interface. 

4.  Save your DAG(s). 

5. Use Apache Airflow monitoring tools to observe your DAG executions. In the ribbon, click on Monitor in Apache Airflow. 

For additional information, please consult the product documentation .   If you’re not already using Fabric capacity, consider signing up for the Microsoft Fabric free trial to evaluate this feature. 

Data Pipelines 

We are excited to announce a new feature in Fabric that enables you to create data pipelines to access your firewall-enabled Azure Data Lake Storage Gen2 (ADLS Gen2) accounts. This feature leverages the workspace identity to establish a secure and seamless connection between Fabric and your storage accounts. 

With trusted workspace access, you can create data pipelines to your storage accounts with just a few clicks. Then you can copy data into Fabric Lakehouse and start analyzing your data with Spark, SQL, and Power BI. Trusted workspace access is available for workspaces in Fabric capacities (F64 or higher). It supports organizational accounts or service principal authentication for storage accounts. 

How to use trusted workspace access in data pipelines  

Create a workspace identity for your Fabric workspace. You can follow the guidelines provided in Workspace identity in Fabric . 

Configure resource instance rules for the Storage account that you want to access from your Fabric workspace. Resource instance rules for Fabric workspaces can only be created through ARM templates. Follow the guidelines for configuring resource instance rules for Fabric workspaces here . 

Create a data pipeline to copy data from the firewall enabled ADLS gen2 account to a Fabric Lakehouse. 

To learn more about how to use trusted workspace access in data pipelines, please refer to Trusted workspace access in Fabric . 

We hope you enjoy this new feature for your data integration and analytics scenarios. Please share your feedback and suggestions with us by leaving a comment here. 

Introducing Blob Storage Event Triggers for Data Pipelines 

A very common use case among data pipeline users in a cloud analytics solution is to trigger your pipeline when a file arrives or is deleted. We have introduced Azure Blob storage event triggers as a public preview feature in Fabric Data Factory Data Pipelines. This utilizes the Fabric Reflex alerts capability that also leverages Event Streams in Fabric to create event subscriptions to your Azure storage accounts. 

Parent/Child pipeline pattern monitoring improvements

Today, in Fabric Data Factory Data Pipelines, when you call another pipeline using the Invoke Pipeline activity, the child pipeline is not visible in the monitoring view. We have made updates to the Invoke Pipeline activity so that you can view your child pipeline runs. This requires an upgrade to any pipelines that you have in Fabric that already use the current Invoke Pipeline activity. You will be prompted to upgrade when you edit your pipeline and then provide a connection to your workspace to authenticate. Another additional new feature that will light up with this invoke pipeline activity update is the ability to invoke pipeline across workspaces in Fabric. 

We are excited to announce the availability of the Fabric Spark job definition activity for data pipelines. With this new activity, you will be able to run a Fabric Spark Job definition directly in your pipeline. Detailed monitoring capabilities of your Spark Job definition will be coming soon!  

To learn more about this activity, read https://aka.ms/SparkJobDefinitionActivity  

We are excited to announce the availability of the Azure HDInsight activity for data pipelines. The Azure HDInsight activity allows you to execute Hive queries, invoke a MapReduce program, execute Pig queries, execute a Spark program, or a Hadoop Stream program. Invoking either of the 5 activities can be done in a singular Azure HDInsight activity, and you can invoke this activity using your own or on-demand HDInsight cluster. 

To learn more about this activity, read https://aka.ms/HDInsightsActivity  

We are thrilled to share the new Modern Get Data experience in Data Pipeline to empower users intuitively and efficiently discover the right data, right connection info and credentials.   

In the data destination, users can easily set destination by creating a new Fabric item or creating another destination or selecting existing Fabric item from OneLake data hub. 

In the source tab of Copy activity, users can conveniently choose recent used connections from drop down or create a new connection using “More” option to interact with Modern Get Data experience. 

Related blog posts

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Welcome to the March 2024 update. We have a lot of great features this month including OneLake File Explorer, Autotune Query Tuning, Test Framework for Power Query SDK in VS Code, and many more! Earn a free Microsoft Fabric certification exam!  We are thrilled to announce the general availability of Exam DP-600, which leads to … Continue reading “Microsoft Fabric March 2024 Update”

JS Tutorial

Js versions, js functions, js html dom, js browser bom, js web apis, js vs jquery, js graphics, js examples, js references, json - introduction.

• JSON stands for J ava S cript O bject N otation

JSON is a text format for storing and transporting data

JSON is "self-describing" and easy to understand

JSON Example

This example is a JSON string:

It defines an object with 3 properties:

Each property has a value.

If you parse the JSON string with a JavaScript program, you can access the data as an object:

What is JSON?

• JSON is a lightweight data-interchange format
• JSON is plain text written in JavaScript object notation
• JSON is used to send data between computers
• JSON is language independent *

* The JSON syntax is derived from JavaScript object notation, but the JSON format is text only.

Code for reading and generating JSON exists in many programming languages.

The JSON format was originally specified by Douglas Crockford .

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Why Use JSON?

The JSON format is syntactically similar to the code for creating JavaScript objects. Because of this, a JavaScript program can easily convert JSON data into JavaScript objects.

Since the format is text only, JSON data can easily be sent between computers, and used by any programming language.

JavaScript has a built in function for converting JSON strings into JavaScript objects:

JSON.parse()

JavaScript also has a built in function for converting an object into a JSON string:

JSON.stringify()

You can receive pure text from a server and use it as a JavaScript object.

You can send a JavaScript object to a server in pure text format.

You can work with data as JavaScript objects, with no complicated parsing and translations.

Storing Data

When storing data, the data has to be a certain format, and regardless of where you choose to store it, text is always one of the legal formats.

JSON makes it possible to store JavaScript objects as text.

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Top Tutorials

Top references, top examples, get certified.

Copy Data Between Data Lake Files Instances Using Python

• How to copy a directory from the source data lake file container to the target data lake file container

Prerequisites

• Two running non-trial SAP HANA data lake Files instances – a source and a target
• Both instances added to SAP HANA database explorer; instructions to add data lake Files container
• Read permissions on the source instance
• Read and write permissions on the target instance
• Client certificates set up on both instances, and a copy of the client certificate and client key files for each instance; instructions to set up certificates
• Python 3.10 or later; download Python

Note that for simplicity, the same client certificate will be used for both the target and the source instance in this tutorial.

In this step, we will create a directory called My_Directory with two subdirectories, Subdirectory_1 and Subdirectory_2 , with a file in each subdirectory. The files can be of any format, however this tutorial will use text files.

Create and save the following text files locally.

Ensure to recall the location of your text files.

In database explorer, upload these files to your source HANA data lake Files storage (HDLFS) instance. Upload File_1.txt , setting the relative path as My_Directory/Subdirectory_1 . This will upload the file and create a new directory My_Directory with subdirectory Subdirectory_1 .

Upload File_2.txt using the relative path My_Directory/Subdirectory_2 .

In this step you will need to access the REST API endpoints for both your source and target HLDFS instances. The REST API endpoint for a given instance can be found by clicking the action menu in SAP HANA Cloud Central.

Create a Python script beginning with the code below and save the file as copy_HDLFS.py . Edit the source and target instance variables with the appropriate REST API endpoints for each of your containers. Edit the certificate variables with the appropriate path to the certificate and key used for both HDLFS containers.

• Append the following code to the end of your copy_HDLFS.py file Python Copy # This script copies a directory including all subdirectories and files from a root directory # in the source HDLFS instance to the target HDLFS instance. # This script is run with the following arguments: # root='root_dir_name' # where root_dir_name is the name of the root directory in the source instance # that is being copied # index=i # (optional) where i is a non-negative integer and the index of the file in the source # instance that will be used as a starting point for the copy -- in other words, the first i # files will be skipped and thus will not be copied; if no value is given, the default index 0 # will be used -- all files will be copied # # Ex. The following arguments would execute a copy from root directory 'TPCH_SF100' starting at the # file at index 42 # py copy_HDLFS.py root='TPCH_SF100' index=42 ################################################################################################### # Importing dependencies import http.client import json from datetime import datetime import ssl import sys ################################################################################################### # Handling arguments if either have been provided # In either order, root and index can be specified by the user in the following format: # py copy_HDLFS.py root='TPCH_SF100' index=42 def assign_arguments(arg_list): args = {} if len(arg_list) <= 3: for arg in arg_list: if arg[:6] == 'root=\'' and arg[-1] == '\'' and not ('root' in args): args['root'] = arg[6:-1] elif arg[:6] == 'index=' and not ('index' in args): try: args['index'] = int(arg[6:]) args['index'] >= 0 except: raise Exception(f'ERROR: Invalid argument {arg}.') else: raise Exception(f'ERROR: Invalid argument {arg}.') else: raise Exception('ERROR: Too many arguments.') return args argument_assignment = assign_arguments(sys.argv[1:]) if 'root' in argument_assignment: ROOT_DIR = argument_assignment['root'] else: raise Exception('ERROR: No root directory was provided. To copy the entire source instance use root=\'\'.') if 'index' in argument_assignment: STARTING_INDEX = argument_assignment['index'] else: STARTING_INDEX = 0 ################################################################################################### # Creating an SSL context using the certificate path and key path variables ssl_context = ssl.create_default_context() ssl_context.load_cert_chain(CERTIFICATE_PATH, KEY_PATH) # Creating container variables for the source and target instances source_container = SOURCE_FILES_REST_API_ENDPOINT.partition('.')[0] target_container = TARGET_FILES_REST_API_ENDPOINT.partition('.')[0] # Creating connections to the source and target instances source_connection = http.client.HTTPSConnection( SOURCE_FILES_REST_API_ENDPOINT, port=443, timeout=30, context=ssl_context) target_connection = http.client.HTTPSConnection( TARGET_FILES_REST_API_ENDPOINT, port=443, timeout=30, context=ssl_context) # Creating JSON request variables needed to access the present source and target HDLFS directories # at the root directory provided json_request_path = f'/{ROOT_DIR}' json_request_url = f'/webhdfs/v1/{json_request_path}?op=LISTSTATUS_RECURSIVE' source_json_request_headers = { 'x-sap-filecontainer': source_container, 'Content-Type': 'application/json' } target_json_request_headers = { 'x-sap-filecontainer': target_container, 'Content-Type': 'application/json' } # Creating request headers for reading and writing binary data from the source and target HDLFS # directories source_request_headers = { 'x-sap-filecontainer': source_container, 'Content-Type': 'application/octet-stream' } target_request_headers = { 'x-sap-filecontainer': target_container, 'Content-Type': 'application/octet-stream' } # http.client connection requests are made and if the request is successful, the read data # is returned def fetch(fetch_connection, fetch_method, fetch_url, fetch_body, fetch_headers): fetch_connection.request( method = fetch_method, url = fetch_url, body = fetch_body, headers = fetch_headers) response = fetch_connection.getresponse() data = response.read() response.close() return data ################################################################################################### # Connecting to the target instance and requesting a list of the current target HDLFS directory at # the root directory provided # If connection is unsuccessful the http.client will raise an exception print('\nConnecting to target instance...') target_json_data = fetch(target_connection, 'GET', json_request_url, None, target_json_request_headers) target_files_dict = json.loads(target_json_data) print('Successfully connected to target instance.\n') # If the root directory already exists in the target instance, the user is prompted to confirm that # they would like to proceed if 'DirectoryListing' in target_files_dict: print(f'WARNING: The directory {ROOT_DIR} already exists at the target HDLFS.') print('Proceeding could result in overwriting files in this directory of the target instance.') user_input = input('Would you like to proceed? (Y/N): ') while user_input not in ['Y', 'N']: print('ERROR: Invalid response. Please enter Y or N.') user_input = input('Would you like to proceed? (Y/N): ') if user_input == 'N': quit() # The start timestamp is declared print('\nStarting copy...') start = datetime.now() print('Start time:\t', start, '\n') # Connecting to the source instance and requesting a list of the current source HDLFS directory at # the root directory provided # If connection is unsuccessful the http.client will raise an exception print('Connecting to source instance...') source_json_data = fetch(source_connection, 'GET', json_request_url, None, source_json_request_headers) source_files_dict = json.loads(source_json_data) print('Successfully connected to source instance.\n') # Accessing the path suffix of each file in the root directory of the source instance source_files_paths = [] for file in source_files_dict['DirectoryListing']['partialListing']['FileStatuses']['FileStatus']: source_files_paths.append(file['pathSuffix']) # Starting with the starting index provided (or the first file if no starting index was provided), # the copy begins cur_index = STARTING_INDEX while cur_index < len(source_files_paths): try: file_path = source_files_paths[cur_index] request_path = f'/{ROOT_DIR}/{file_path}' offset = 0 length = 10000000 read_length = length merge_count = 0 to_merge = {'sources': []} list_of_temp = [] # While each chunk of bytes read continues to be the length of bytes requested, indicating # that the EOF has not been reached, more bytes are read while read_length == length: source_request_url = f'/webhdfs/v1/{request_path}?op=OPEN&offset={offset}&length={length}' source_data = fetch(source_connection, 'GET', source_request_url, None, source_request_headers) read_length = len(source_data) # If the first request returns less than 10MB, the entire file has been read and can be # written to a file under the same name in the target location, without creating any # temporary files if offset == 0 and read_length < length: target_request_url = f'/webhdfs/v1/{request_path}?op=CREATE&data=true' target_data = fetch(target_connection, 'PUT', target_request_url, source_data, target_request_headers) print(f'Created and wrote {read_length} bytes to {request_path}') # Otherwise a temporary file is created for the current read and each subsequent read; # the files will later be merged else: merge_count += 1 temp_path = request_path[:-8] + str(merge_count) + '.parquet' target_request_url = f'/webhdfs/v1/{temp_path}?op=CREATE&data=true' target_data = fetch(target_connection, 'PUT', target_request_url, source_data, target_request_headers) print(f'Created temporary file {temp_path}') list_of_temp.append(temp_path) temp_to_merge = {'path': temp_path} to_merge['sources'].append(temp_to_merge) offset += length # If there are files to merge, they are merged here and delete the temporary files if merge_count != 0: cur_file_bytes_read = offset + read_length print(f'Read and wrote {cur_file_bytes_read} bytes to {merge_count}', f'temporary files for file {request_path}') # Creating the file where we will merge all temporary files to target_request_url = f'/webhdfs/v1/{request_path}?op=CREATE&data=true' target_data = fetch(target_connection, 'PUT', target_request_url, None, target_request_headers) print(f'Created {request_path}') # Merging the files to the merge destination file target_request_url = f'/webhdfs/v1/{request_path}?op=MERGE&data=true' target_data = fetch(target_connection, 'POST', target_request_url, json.dumps(to_merge), target_request_headers) print(f'Merged {merge_count} files to {request_path}') # Deleting the temporary files after the merge is complete to_delete = {'files': to_merge['sources']} target_request_url = f'/webhdfs/v1/?op=DELETE_BATCH&data=true' target_data = fetch(target_connection, 'POST', target_request_url, json.dumps(to_delete), target_request_headers) print(f'Deleted {merge_count} temporary files') # If an exception is raised, the error is printed and arguments are provided to rerun the # script beginning with the file in which the error occured except Exception as error: print(error) print('To rerun this script begining with the most recently accessed file, run:', f'\npy {sys.argv[0]} root=\'{ROOT_DIR}\' index={cur_index}') quit() # If any other error occurs, arguments are provided to rerun the script beginning with the file # in which the error occured except: print('To rerun this script begining with the most recently accessed file, run:', f'\npy {sys.argv[0]} root=\'{ROOT_DIR}\' index={cur_index}') quit() else: cur_index += 1 end = datetime.now() print(f'Successfully copied {ROOT_DIR} from index {STARTING_INDEX}', 'from source instance to target instance.') print('End time:\t', end) print('Elapsed time:\t', end - start)

To copy the directory My_Directory from the source to the target HDLFS, execute the following in command prompt.

Verify that My_Directory as well as its contents are now visible in the target container.

Congratulations! You have now copied the directory My_Directory between HDLFS instances.

Which of the following statements are true?

• The REST API is leveraged in the Python script to copy data between HDLFS instances.
• The Python script can be used to copy large files and directories between source and target Data Lake Files instances.
• Only `.txt` files can be copied using the Python script.
• Set up a directory in the source data lake Files instance
• Set up a Python script
• Set up a copy script
• Run the copy script
• Knowledge check

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