Solution — Case Studies (GCP Professional Cloud Architect)

“Excerpt from https://www.udemy.com/course/gcp-architect-am/?couponCode=GCP-OCT”

During the exam for the Cloud Architect Certification, some of the questions may refer you to a case study that describes a fictitious business and solution concept to provide additional context to exam questions. There are four case studies as described below. The solution is narrated in a youtube video linked below.

Exam Case Study & Solution

1. EHR Healthcare — https://services.google.com/fh/files/blogs/master_case_study_ehr_healthcare.pdf

EHR Healthcare is a leading provider of electronic health record software to the medical industry. EHR Healthcare provides their software as a service to multi-national medical offices, hospitals, and insurance providers.

Problem Statement:- Cloud Architecture for on-prem to cloud migration solution & scale problem

Solution:

Infrastructure

• Google’s IaaS and PaaS solution for data canters, Global VPC
• Multi-regional replication for DR
• Hybrid Connectivity — Cloud Interconnect (99.99% uptime SLA), Cloud VPN
• Dedicated Interconnect for high performance connection between on-premises and GCP

Applications

• Kubernetes Deployment — GKE
• Cloud and On-premise container based environments management and integration — Anthos
• Future API based integration — Apigee
• Predictions — AI Platform
• Data Ingestion — Streaming (Pub/Sub), Batch (Cloud Storage)
• Process — Dataflow, Cloud Composer

Databases

• MySQL, MS SQL — Cloud SQL
• Redis — Cloud Memorystore
• MongoDB — Cloud Firestore

Monitoring

Cloud Monitoring — Alerts and notifications, Charts and dashboards

Cloud Logging — Automatically ingest audit and platform logs, manage retention and policies.

Continuous Deployment

• Use Terraform for Infrastructure as Code
• Use Cloud Source Repositories for storing the source code
• Use Cloud Build for deployment and orchestration.
• Use Artifact Registry for container images

2. Helicopter Racing League- https://services.google.com/fh/files/blogs/master_case_study_helicopter_racing_league.pdf

Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.

Problem Statement:-Cloud AI & ML, telemetry and streaming problem

Solution:

Transcoding

• Use Preemptible instance for VM based encoding solution
• Containerise the encoding solution and manage using Kubernetes engine.

TV Box Telemetry

• App Engine, Pub/Sub, Dataflow, BigQuery, Cloud Composer and Cloud Monitoring increase telemetry and create additional insights.

Live Video Latency

• Use HA configuration of Cloud VPN for connectivity between mobile data centers and google cloud.
• Use Cloud CDN for delivering content with speed, efficiency and reliability closer to the users.
• Use Cloud Storage with multi-regional buckets to serve contents.
• Use PerfKit Benchmarker to get visibility into metrics like latency, throughput, and jitter.
• Google Cloud offers Network Intelligence Center for comprehensive and proactive monitoring, troubleshooting, and optimization capabilities

AI & ML

• Use AI platform for predictive efficiency
• Use TensorFlow Deep Learning VM instances

Analytics

• Use BigQuery as a data mart for processing of large volume of data.
• Use Looker for embedded analytics.
• Big Query streaming API and ML solution can create additional insights for increasing fan engagements.

3. TerramEarth — https://services.google.com/fh/files/blogs/master_case_study_terramearth.pdf

TerramEarth manufactures heavy equipment for the mining and agricultural industries.. They currently have over 500 dealers and service centers in 100 countries. There are 2 million TerramEarth vehicles in operation currently, and they see 20% yearly growth. Their mission is to build products that make their customers more productive.

Problem Statement:- Cloud Automation, Operations and API Ecosystem problem

Solution:

Data Replication:

• Stream critical data from vehicles to Cloud Bigtable to drive analytics in real time.
• Sensor Device -> HTTPS Gateway Device -> Pub/Sub -> Dataflow -> Bigtable -> GKE (Application & Presentation)
• BigQuery partitioning by timestamp for Home base upload and unified analytics.

Data processing

• Cloud Dataflow for serverless unified (batch & stream) ETL

ML Engine or AutoML Tables

• Use Vertex AI for ML lifecycle to forecast anticipated stock needs to assist with just-in-time repairs.

Vehicles — Home Base Connected

Device management & upload

• Cloud IoT Core
• IoT devices -> Cloud Pub/Sub
• Cloud Dataflow -> Cloud Storage

Cloud Operations:

• Managed and Serverless services
• Network Intelligence Center for for monitoring, verification and optimization.
• Network Connectivity Center & Security Command Center for holistic security view.
• Cloud Monitoring for real time visibility
• Google Cloud KMS for Key Management

API’s ecosystem:

• Apigee (X) to manage and monitor APIs, it create an abstraction layer to connect to different interfaces.
• Apigee Developer Portal lets you build a self service portal for internal and external developers.
• Build and Deploy API’s on Google Kubernetes Engine

CICD:

• Use Cloud Source Repository, Artifact Repository, Cloud Build for CICD operations.
• Use Spinnaker to deploy on Kubernetes with Blue Green and Canary deployments.

Remote Workforce:

• G Suite with integrated Cloud IAM.
• Cloud Data Loss Prevention for sensitive data protection.
• Use Connected sheets with BigQuery to collaborate with integrated security controls.

4. Mountkirk Games — https://services.google.com/fh/files/blogs/master_case_study_mountkirk_games.pdf

Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They have recently started expanding to other platforms after successfully migrating their on-premises environments to Google Cloud. Mountkirk Games is building a new multiplayer game that they expect to be very popular.

Problem Statement:- Cloud Auto-scaling, Gaming analytics Problem

Solution:

• Cloud Spanner with relational features, horizontal scaling and 99.999% availability across regions.
• Google Kubernetes Engine(GKE) to deploy game’s backend as microservices
• Google Load Balancing for worldwide seamless autoscaling
• Pub/Sub, Dataflow and BigQuery for Stream analytics
• Looker for player insights and analytics
• GPUs hardware accelerators on GKE.
• Cloud Datastore for transactional game state
• Cloud Storage for storing game activity logs and analysed using BigQuery
• Cloud Pub/Sub for buffering of live and late data
• Cloud Dataflow for bulk and stream processing
• BigQuery for storage and analytics; this can also contain the 10 TB historic data
• Managed and Serverless services for dynamic scaling, minimal cost and operations.
• Cloud Operations metrics and APM functionality for proactive troubleshooting.

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