TerramEarth Case Study 2022 — GC Architect design evaluation

Continuing my journey as a Cloud Architect, I am trying to take case studies and provide my understanding of end-to-end solutions starting from design, infrastructure landscape, HA/DR, networking, trade-offs and migration planning, etc. Here is the first of many more to come.

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.

• 100 countries Global presence — Multi regional, provide HA, low latency with no single point of failure
• Make customers more productive — GSuite, On-Time access to the system, and Real-Time data update

Solution concept

There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth.

• 2 million TerramEarth vehicles with 20% growth - Need scalability
• Real-time data

Vehicles collect telemetry data from many sensors during operation.

• IOT Architecture

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.

2 Type of data ingest:

1. Real-Time Data: used for fleet management
2. Sensor data: Batch data uploaded to on premise-data center

Each vehicle usually generates 200 to 500 megabytes of data per day.

• Data-intensive and data-driven solution needed

Existing technical environment

TerramEarth As In Architecture

TerramEarth’s vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world.

• Multi-region and zone deployments
• Already in Google Cloud
• CDN — most probable for caching and low latency

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.

• On-Premise data center — will need migration to Google Cloud in future
• Legacy systems — Will need to be moved to Google Cloud with GKE or CE

The private data centers have multiple network interconnects configured to Google Cloud.

• On-Premise connection to GCP via interconnect
• Hybrid Networking

The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics.

• Part of data already migrated to GCP that’s has a front end. Should be using App Engine with three-tier applications.
• Analytics Dashboard exists for AI/ML — Use Looker

Business requirements

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

• UI Dashboard already exists. Data to be exported to BigQuery for AI/ML for inventory prediction. Data Flow for dashboard
• Use BQ ->Vertex A->Cloud Dataflow

Decrease cloud operational costs and adapt to seasonality.

• Managed Services
• Computing- use serverless products( app engine vs compute engine)
• Restart not an issue use preemptive VMs to optimize cost
• Create separate projects for Dev Test Prod env — auto-scaling

Increase speed and reliability of development workflow.

• CI/CD Cloud Operations to capture Audit and Network Logs (VPC Flow Logs) Network Intelligence to monitor performance and topology

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

• GitHub
• Remote access using VPNCloud Identity and Access Mgt system
• (Private Google Access, IAP with signed headers)

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

• API backend architecture
• Apigee — Shared and secured apis

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.

• API backend architecture
• Apigee
• Gradual/piece by piece migration to GCP

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

• Kubernetes or Cloud Run?
• CI-CD Automation(Cloud Build, Spinnaker, Jenkins)
• Cloud Registry for Container Image

Allow developers to run experiments without compromising security and governance requirements

• GDPR?
• Separate env Test and Prod

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.

• UI portal using API connected to the backend — 3 tier arch

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

• KMS and secrets? — encryption key and gke keys
• Improve and standardize tools necessary for application and network monitoring and troubleshooting.
• Cloud Monitoring and logging :
• Flow log and Audit logs

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.

• Reduce latency
• Decommission data center -? Datastream and batch to cloud

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 the operations of our dealerships.

• Advantage — Already on google Cloud
• Future plan?
• Improve operation and dealership turnaround time and productivity

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.

• Give secured access to apis — Apigee