google cloud terramearth case study

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Google cloud – terramearth case study.

TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.

Key points here are 500 dealers and service centers are spread across the world and they want to make their customers more productive.

Solution Concept

There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth. Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base. Each vehicle usually generates 200 to 500 megabytes of data per day

Key points here are TerramEarth has 2 million vehicles. Only critical data is transferred in real-time while the rest of the data is uploaded in bulk daily.

Executive Statement

Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes. After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in-class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.

Key point here is the company wants to improve further in operations, customer experience, and partner ecosystem by allowing them to reuse the data.

Existing Technical Environment

TerramEarth’s vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics.

Key point here is the company is hosting its infrastructure in Google Cloud and private data centers. GCP has web frontend and vehicle data aggregation & analysis. Data is sent to private data centers.

Business Requirements

Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible.

• Cloud IoT core can provide a fully managed service to easily and securely connect, manage, and ingest data from globally dispersed devices.
• Existing legacy inventory and logistics management systems running in the private data centers can be migrated to Google Cloud.
• Existing data can be migrated one time using Transfer Appliance.

Decrease cloud operational costs and adapt to seasonality.

• Google Cloud provides configuring elasticity and scalability for resources based on the demand.

Increase speed and reliability of development workflow.

• Google Cloud CI/CD tools like Cloud Build and open-source tools like Spinnaker can be used to increase the speed and reliability of the deployments.

Allow remote developers to be productive without compromising code or data security.

• Cloud Function to Function authentication

Create a flexible and scalable platform for developers to create custom API services for dealers and partners.

• Google Cloud provides multiple fully managed serverless and scalable application hosting solutions like Cloud Run and Cloud Functions
• Managed Instance group with Compute Engines and GKE cluster with scaling can also be used to provide scalable, highly available compute services.

Technical Requirements

Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations.

• Google Cloud API Gateway & Cloud Endpoints can be used to provide an abstraction layer to expose the data externally over a variety of backends.

Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments.

• Google Cloud provides DevOps tools like Cloud Build and supports open-source tools like Spinnaker to provide CI/CD features.
• Cloud Source Repositories are fully-featured, private Git repositories hosted on Google Cloud.
• Cloud Build is a fully-managed, serverless service that executes builds on Google Cloud Platform’s infrastructure.
• Container Registry is a private container image registry that supports Docker Image Manifest V2 and OCI image formats.
• Artifact Registry is a fully-managed service with support for both container images and non-container artifacts, Artifact Registry extends the capabilities of Container Registry.

Allow developers to run experiments without compromising security and governance requirements

• Google Cloud deployments can be configured for Canary or A/B testing to allow experimentation.

Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints.

Use cloud-native solutions for keys and secrets management and optimize for identity-based access

• Google Cloud supports Key Management Service – KMS and Secrets Manager for managing secrets and key management.

Improve and standardize tools necessary for application and network monitoring and troubleshooting.

• Google Cloud provides Cloud Operations Suite which includes Cloud Monitoring and Logging to cover both on-premises and Cloud resources.
• Cloud Monitoring collects measurements of key aspects of the service and of the Google Cloud resources used
• Cloud Monitoring Uptime check is a request sent to a publicly accessible IP address on a resource to see whether it responds.
• Cloud Logging is a service for storing, viewing, and interacting with logs.
• Error Reporting aggregates and displays errors produced in the running cloud services.
• Cloud Profiler helps with continuous CPU, heap, and other parameters profiling to improve performance and reduce costs.
• Cloud Trace is a distributed tracing system that collects latency data from the applications and displays it in the Google Cloud Console.
• Cloud Debugger helps inspect the state of an application, at any code location, without stopping or slowing down the running app.

Reference Cellular Upload Architecture

Batch Upload Replacement Architecture

• Google Cloud – TerramEarth case study

2 thoughts on “ Google Cloud – TerramEarth Case Study ”

https://jayendrapatil.com/google-cloud-terramearth-case-study/

Good page. Thank you.

“Cloud Storage and other AWS managed services like Pub/Sub, Dataflow and BigQuery:”

AWS 🙂 – Can’t complain too much, I call my kids by the wrong names sometimes.

thanks a lot Gary, multi-cloud issues 🙂 – Corrected the same

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This is my solution design/implementation for the TerramEarth sample case study provided by Google ( https://cloud.google.com/certification/guides/cloud-architect/casestudy-terramearth-rev2 ) - solution may vary due upgrade of Google Cloud Platform.

fdicarlo/GCP_TerramEarth

Folders and files, repository files navigation.

Architecture implementation Technical implementation

Sample case study: TerramEarth

TerramEarth manufactures heavy equipment for the mining and agricultural industries. About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.

1. Solution concept

There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.

Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles.

2. Existing technical environment

TerramEarth’s existing architecture is composed of Linux and Windows-based systems that reside in a single U.S. west-coast-based data center. These systems gzip CSV files from the field and upload via FTP and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is three weeks old.

With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to four weeks while they wait for replacement parts.

3. Business requirements

• Decrease unplanned vehicle downtime to less than one week
• Support the dealer network with more data on how their customers use their equipment to better position new products and services
• Have the ability to partner with different companies - especially with seed and fertilizer suppliers in the fast-growing agricultural business - to create compelling joint offerings for their customers

4. Technical requirements

• Expand beyond a single data center to decrease latency to the American Midwest and East Coast
• Create a backup strategy
• Increase security of data transfer from equipment to the data center
• Improve data in the data warehouse
• Use customer and equipment data to anticipate customer needs

5. Application 1: Data ingest

A custom Python application reads uploaded data files from a single server, writes to the data warehouse

• 128 GB of RAM
• 10 TB local HDD storage

6. Application 2: Reporting

An off-the-shelf application that business analysts use to run a daily report to see what equipment needs repair. Only two analysts of a team of 10 (five West Coast, five East Coast) can connect to the reporting application at a time.

• Windows Server 2008 R2
• 32 GB of RAM

Data warehouse

• RedHat Linux
• 4x 6TB HDD in RAID 0

7. Executive statement

Our competitive advantage has always been in our manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I’m concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.

Professional Cloud Architect - Google Cloud Certification Guide by Konrad Clapa, Brian Gerrard

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TerramEarth

We will now look at the TerramEarth case study. Again, we will review what can be gleaned from the online documentation. At the end of the case study, we will provide an analysis. We would like you to write down the services that jump out to you as you are reading and compare your findings to our analysis:

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How To Work On GCP Case Study – TerramEarth

What is a case study .

In Google Cloud Architect certification examination, some of the questions may refer you to a GCP case study that presents a real-world like business solution concept with fictitious company names. I recommend that you work on these case study solutions as a part of your prior study plan and be ready with the solution answers. Below are the four case studies that may appear in the exam.

• EHR Healthcare
• Helicopter Racing League
• Mountkirk Games
• TerramEarth

If you are planning to prepare for GCP Professional Cloud Architect exam, read my blog for tips, resources and more helpful information – Six steps to Google Professional Cloud Architect Certification Blog

How many questions are expected in the actual exam ?

Out of total 50 exam questions, we may expect 10 questions based on any two out of above four case studies. Not to underestimate the importance, it is critical to prepare all scenarios during the course of study. We do not really get any time to read through the case study document during the actual exam, while the clock is ticking fast.

Checkout 200+ practice exam questions here – Google Professional Cloud Architect Certification – Practice Exam

When to start working on case study solutions ?

For any business use case solutioning, it is expected that you have basic understanding of the major Google Cloud Platform products and services. Hence the best time to start working on the case studies is after you have finished learning all the modules of GCP course study path.

The Approach

In this article, I propose an iterative approach to work on any industry use case solution. It has four major components as shown below.

Step 1: Identify GCP products & services

• Read the use case document carefully looking for any clues in each requirement.
• Identify what GCP product/services would best fit the solution.
• List down all the product/services on the solution paper as draft version.

Step 2: Identify knowledge gaps

• It may happen that you identify knowledge gaps with respect to few GCP product/service at this stage and it is absolutely fine.
• Go back to GCP documentation or video lessons to close those gaps.

Step 3: Refer Best Industry practices

• GCP has great quality documentation for industry best practices. Identify which practices can best fit your solution.
• Check if any special requirements on system performance or reliability design need any changes to draft version.

Step 4: Draw the solution

• It is essential for architect role to have skills of drawing architecture diagrams. The best way is to use easy paper and pen style at the beginning.
• Tools like draw.io or visual paradigm can be used later to sketch final version. GCP products & flow templates are also available within these tools. It helps to reduce time and effort required to sketch the solution diagrams.

Tips for the case study

• ‘Think through’ the solution yourself. Do not look at ready solutions on internet before your solution is completed.
• I recommend to re-iterate from step 1 to step 4 process at least 3 to 4 times, of course with sufficient time breaks in between. The solution evolves during each iteration and final version looks much better and presentable one.
• Discuss your solution among the co-learners in the group and get to know others’ perspective to see whether any enhancements are still required in the design.

With this approach, let’s take a look at the case study solution for TerramEarth

TerramEarth Company overview

TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.

Solution concept

There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth. Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base. Each vehicle usually generates 200 to 500 megabytes of data per day.

Products/services Identified:

• Cloud IoT Core is fully managed service for ingestion of telemetry data from sensors on the vehicles.
• Cloud Pub/Sub is used for streaming / real-time data ingestion.
• Cloud Storage for batch data processing from vehicles which upload data after returning to home base.
• Cloud BigTable is used for storing time series data from sensors.

Existing technical environment

TerramEarth’s vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world . A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics.

• Cloud Dataflow is used for real-time and batch data aggregation pipelines.
• BigQuery is serverless data warehouse, enables scalable data analysis.
• HTTPS Global Load balancer for servicing global clients with improved service availability.

Business requirements

– Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in time repair where possible. – Decrease cloud operational costs and adapt to seasonality. – Increase speed and reliability of development workflow. – Allow remote developers to be productive without compromising code or data security . – Create a flexible and scalable platform for developers to create custom API services for dealers and partners.

• Machine Learning either with BigQueryML or AI platform is used for predictions of vehicle malfunction.
• CI/CD – Cloud Source Repositories, Cloud Build is used for DevOps automation which helps to increase speed of development workflow.
• Cloud IAM is used to provide remote developers access permissions to GCP project resources.

Technical requirements

– Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations. – Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable cloud environments. – Allow developers to run experiments without compromising security and governance requirements – Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints. – Use cloud-native solutions for keys and secrets management and optimize for identity based access. – Improve and standardize tools necessary for application and network monitoring and troubleshooting.

• Apigee Hybrid allows to host APIs in GCP and on-premises environments.
• Cloud Source Repository, Cloud Build, Container Registry for modernise CI/CD pipelines.
• Google Kubernetes Engine for distributed container based workload deployment and management.
• Sandbox environment is used by developers for experimentations with GCP products.
• Developer portal helps internal and partner developers to search and test TerramEarth’s APIs.
• Cloud KMS is used to create and manage cryptographic keys for access to applications and resources.
• Cloud Operations is suite of tools like Logging, Trace, Profiler, Monitoring, Debugger

Executive statement

Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes. After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in-class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.

Proposed Solution Diagram

Sample Case Questions  

Below are some sample questions which can appear in the certification exam – 

• Taking into consideration the business requirement, technical requirements and executive statement, what replacement would recommend for their data warehousing needs?
• How to deliver APIs that meet their business requirements?
• How to design the data ingestion layer for handling massive amount of data and also retrieving as quickly as possible?
• How should you minimize costs while analyzing raw telemetry data?

Hope you find this case study approach as useful support for examination preparation.

Written exclusively for ReviewNPrep.com – By Manoj P. (Connect with me on  LinkedIn ).

Manoj Patil

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Google Professional Cloud Architect Case Studies

This lesson will help you prepare for the Professional Cloud Architect Exam. We cover the 4 case studies presented in the exam guide, explain  what they are, why they are important, and how to use them to prepare for the exam.

Learning Objectives

Examine the 4 case studies presented in the exam guide:

• EHR Healthcare
• Helicopter Racing League
• Mountkirk Games
• TerramEarth

Intended Audience

Anyone planning to take the Professional Cloud Architect Exam.

Prerequisites

Basic knowledge of GCP.

Daniel began his career as a Software Engineer, focusing mostly on web and mobile development. After twenty years of dealing with insufficient training and fragmented documentation, he decided to use his extensive experience to help the next generation of engineers.

Daniel has spent his most recent years designing and running technical classes for both Amazon and Microsoft. Today at Cloud Academy, he is working on building out an extensive Google Cloud training library.

When he isn’t working or tinkering in his home lab, Daniel enjoys BBQing, target shooting, and watching classic movies.

Latest EHR Healthcare Case Study (2024) - Google Cloud (GCP) Architect

Possible architecture solution.

• Anthos : Anthos will help to manage both cloud and On-prem
• Database : Cloud SQL, Memorystore(instead of Redis) and Datastore (instead of Mango DB)
• Cloud Storage Log Retention
• Batch : Cloud Storage > Dataflow > BigQuery - Make predictions and generate reports on industry trends
• Streaming : Pub/Sub > Dataflow > BigQuery
• Cloud CDN : Reduced latency
• CI/CD : Container Registry, Cloud Build, Kubernetes Engine

Reference Architecture Solution diagram

There will be multiple possible solutions for this use case, to understand the implementation better we have give reference architecture which is one of possible solution Below reference architecture depicts different aspect of Gaming Analytics cloud Platform solution like gaming server on GKE, Batch and stream pipeline and common cross cutting infrastructure components :

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Exam professional cloud architect topic 9 question 1 discussion.

For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?

• A. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
• B. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.
• C. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
• D. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.

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