Once again, I’m going to use RapGenius annotations to mark and analyze parts of the case studies.

Individual Cases

1. Dress4Win

https://genius.it/cloud.google.com/certification/guides/cloud-architect/casestudy-dress4win

This is a web development company running existing servers in a colocated data centre.  They care about IAM, ability to scale and lowering costs.

Summary:

Migrate Existing components to managed services

• MySQL -> CloudSQL
• Apache Beam -> Dataflow
• Java Tomcat -> potentially App Engine
• Nginx static content -> Cloud Storage and CDN
• Redis has no equivalent yet
• Hadoop/Spark servers -> Dataproc
• MQ servers -> Cloud PubSub
• Monitoring servers -> Stackdriver suite
• Back up their images, logs onto Cloud Storage

Other

• Use automated/repeatable resource creation tools such as Cloud SDK and Cloud Deployment Manager
• Use managed services (aka avoid Compute Engine)
• Use VPN tunnels (CloudVPN and Cloud Router)

2. TerramEarth

https://genius.it/cloud.google.com/certification/guides/cloud-architect/casestudy-terramearth

This is a industrial vehicle manufacturer that streams live data about their vehicles in the field through cell network, or batch uploaded through their servicing centres.  They care mostly about turnaround time on their data, not costs.

Summary:

• Case study is almost completely data oriented
• Two sources of big data: 90% from batch uploads from service partners, 10% realtime uploads through cell network
• Pub sub, cloud storage and dataflow for live data from mobile networks
• Cloud storage, dataflow for data from service centres
• Store data in BigQuery warehouse
• Alternative to cloud storage/pubsub is to VPN data from their FTP to dataflow or interconnect
• IAM issues on how to share data with their dealers and potential partners

3. MountKirk

https://genius.it/cloud.google.com/certification/guides/cloud-architect/casestudy-mountkirkgames

This is a mobile gaming company starting project from scratch.  They want to focus on scaling and analysis of game data.

Summary:

Game server

• Autoscaling Managed instance groups for custom Linux distros on Compute Engine for game server
• NoSql database, probably Cloud Datastore for game server (BigTable is more for analytics in petabytes of data).

Analytics

• Pub sub to handle/buffer data from mobile phones and game servers
• Cloud storage to handle uploaded files from mobile phones (and maybe cloud functions to process them)
• Dataflow to process all of the cloud storage and pub sub data
• Bigquery data warehouse to house all the analytics
• Maybe Datalab for analysis and visualization of the data

4. JencoMart

https://genius.it/cloud.google.com/certification/guides/cloud-architect/casestudy-jencomart

This is a retailer with an online shop.  They want to expand into Asia.

Summary:

• Requires Global load balancing and low latency in Asia (interconnects/peering?)
• Potentially use App Engine for their LAMP stack (Customer Loyalty Portal)
• Use of custom Compute Engine (RAM and CPU) with persistent disks
• Google Cloud Storage 100 TB
• Can move one of the DBs (Postgres not Oracle) to CloudSQL
• Want to explore data analytics in the future (BigQuery, Dataflow, Dataproc, Datalab
  Cloud Machine Learning / Tensorflow)

Summary by Product