Latest TerramEarth Case Study - Google Cloud (GCP) Architect (2024)

We will be sharing our analysis for GCP Professional Cloud Architect - TerramEarth is a GCP Master Case Study for IoT to help you in your study and exam preparation.

This case study has been around for a while and was recently modified on May 1, 2021 to coincide with the latest version of the Architect certificate exam. This is the most complicated and challenging case study because it doesn't go into great detail, but there are a lot of phrases linked to vehicles, such as fleet management, stock management, inventory, and logistics.

The Internet of Things (IoT) is gaining in popularity and importance. Surprisingly, it integrates with cloud platforms like Google Cloud Platform. As a result, the inclusion of TerramEarth as a master case study on IoT in the GCP Professional Cloud Architect certificate makes perfect sense. Lets begin

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.

Key points here are :

• They have a global audience with 500 dealers and service locations in 100 countries.

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 a population of 2 million vehicles.
• Expected 20% yearly growth
• Real time data : To aid fleet management, a small subset of vital data is transmitted in real time from the vehicles.
• Daily Batch data : Every day when the vehicles return to home base, the rest of the sensor data is collected, compressed, and uploaded daily.
• Each vehicle produces 200 to 500 MB of data each day on average. Volumes of data that we are talking about in here is huge.

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 points here are :

• Using Google Cloud
  • Data aggregation and analysis infrastructure
  • Web front end for dealers and customers that provides stock management and analytics capabilities.
• Private Data Center
  • They are hosting legacy inventory and logistics management systems in Private Data Center.
  • They have a couple of manufacturing plants, which sends census data to private data centers.
  • Private data centers also have multiple networking techniques to connect to the Google cloud

Business requirements

• Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-intime 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.

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 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.

Key points here are :

• Customers should have access to the best-in-class online fleet management services:
  • Improve the operations of dealerships
  • Boost their clients productivity by minimizing vehicle downtime. If you can quickly detect faults and ship components to dealerships, you can perform Just-In-Time repairs.
• Autoscaling, DevOps and SRE Elements:
  • They'd also like to provide auto-scaling for the box and the SRT element, also want to improve and standardize monitoring and troubleshooting tools for apps and networks.
  • Modernize their CI/CD workflows to accommodate container workloads Container-based (most significant things to notice here).
  • Make it possible for remote developers to be productive.
• Flexible and scalable platform for developers to create custom apps for dealers and partners:
  • Create a new abstraction layer using HTTP API, to provide an API around their legacy systems and enable a gradual migration to the cloud.
  • self-service portal that allows all developers to work together, whether they're internal or external. provide them the ability to start new projects, request resources, and control who has access to their files and manage Endpoints.
  • Make use of cloud native solutions for keys and secret management.

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 points here are :

5 year strategic plan :
• Want to create a partner ecosystem of new products by enabling access to their data
• More autonomous operations you would want, the more you would want to go to realtime streaming.
• Move legacy systems to the cloud

Reference Architecture Solution diagram

To minimize the vehicle down time, best approach for Just-In-Time repair is to capture as much data as possible in real time. So you can have IOT core managing the IOT devices which are installed on the vehicles and you can stream the data to PubSub> Dataflow > BigQueary and you can use the AI platform or autoMl or BigQuearyML to generate your intelligence.

Below architecture depicts different aspect like real-time, batch flow, CI-CD (Cloud Build, Spinnaker, Jenkins), Apigee, Secret Managment (KMS, Cloud IAM), GKE, Cloud Monitoring and logging :