Case Study Review: TerramEarth
Credit goes to Indro Bhattacharya for this series of case study posts
As most of you know by now, the Google PCA (Professional Cloud Architect) exam was revamped on May 1st, 2021. With the new version of the exam, and having cleared it myself last month, I noticed some significant changes. Some of the key changes from the previous version of the exam are:
The questions are more conceptual than straightforward
Introduction of new areas like Anthos and MLOps
Longer questions
Multiple services being tested on a question (like a true architect!)
All new case studies
In this blog post, I will outline how I went about solving the new case studies. I will post the exact document I wrote, and which since May 14th 2021, over 240 Googlers across the globe have used as part of their exam prep. I want to thank the many Googlers who took time to comment and improve the document to get it to its current state. Big shout out to Iman for allowing me to post this on his amazing website. I hope this material will help in your prep as well.
If you haven’t already, please read the exam deep dive to understand the overall strategy and key objectives to study for the Professional Cloud Architect exam.
All the best!
TerramEarth
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.
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 (IoT Core) 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.
Business Requirements
Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in time repair where possible.(AI Platform)
Decrease cloud operational costs and adapt to seasonality. (Managed Services)
Increase speed and reliability of development workflow. (CI/CD)
Allow remote developers to be productive without compromising code or data security.(Private Google Access, IAP with signed headers)
Create a flexible and scalable platform for developers to create custom API services for dealers and partners.(Apigee)
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. (Apigee)
Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments. (GKE)
Allow developers to run experiments without compromising security and governance requirements (Separate Project/IAM)
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. (IAM, Apigee)
Use cloud-native solutions for keys and secrets management and optimize for identity based access (Cloud KMS, Secret Manager)
Improve and standardize tools necessary for application and network monitoring and troubleshooting (Cloud Operations)
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.
Basic evaluation
Client
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.
Values
Already on GCP
Multiple network interconnects in place between OnPrem and GCP
Web Front end running on GCP
Immediate Goals
Minimize Vehicle Downtimes
Provide best in class online Fleet management services
Improve dealership
Technical evaluation
Requirements
Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in time repair
Technical Watchpoints
The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics.
Proposed Solution
Use AI Platform to create prediction models
BigQuery for handling real time data to facilitate fleet management
Requirements
Decrease cloud operational costs and adapt to seasonality
Proposed Solution
IoT Core, Pub/Sub and Dataflow as we need to decouple the messages ingestion and processing
Requirements
Increase speed and reliability of development workflow.
Technical Watchpoints
Modernize all CI/CD pipelines
keys and secrets management and optimize for identity based access
Standardize tools necessary for application and network monitoring and troubleshooting
Proposed Solution
Modernize CI/CD with Cloud Build and Deployment Manager
Cloud KMS, Secret Manager
Cloud Operations to capture Audit and Network Logs (VPC Flow Logs) Network Intelligence to monitor performance and topology
Requirements
Remote developer productivity
Technical Watchpoints
Allow developers to run experiments without compromising security and governance requirements
Proposed Solution
Use Identity Aware Proxies (IAP) and host the sandbox project in a separate folder with appropriate policies in place (IAM and network policies)
Requirements
Create custom API services for dealers and partners
Technical Watchpoints
Create a new abstraction layer for HTTP API access to their legacy systems
Self-service portal to create projects, request resources for analytics jobs, and centrally manage APIs
Proposed Solution
Apigee as central portal for API access management, self service and monetization.
GKE as backend service aggregating On-Prem data and Analytic data, requesting analytics jobs
Requirements
Interconnect with private data center
Proposed Solution
Cloud Router + Interconnect(Partner or Dedicated) for interconnect with private datacenter
Products: AI Platform (Predictions), IoT Core (for managing devices and creating a bridge to stream data), Pub/Sub (as endpoint to ingest streaming data from IoT devices) Dataflow (for processing), Terraform and Deployment Manager (CI/CD), Cloud KMS & Secrets Manager (reliability of dev workflow), Cloud Operations Suite (for monitoring), Cloud IAM and IAP (Remote dev productivity), Apigee (API layer for access to Legacy systems and self service portal)
Stay tuned for case study reviews on each of the four business cases on the exam. In the meantime, don’t forget to check out the exam deep dive! Best of luck to you throughout your studies, you’ll do GREAT!
