The 3 Case Studies

Case Studies: On the Professional Cloud Architect exam, you’ll be provided with case studies that will be required for a significant amount of questions. A few sample case studies that are provided online. Please note that these are subject to change by Google Cloud at any time without notice. These sample case studies will describe a fictitious business, a solution concept, some background on their existing technical environment, any business and technical requirements, and an executive statement. As you read through these case studies, think about what types of questions you could be asked based on them. Parse for business requirements, technical requirements, keywords, constraints, timelines, and so on. 

1. Mountkirk Games
https://cloud.google.com/certification/guides/cloud-architect/casestudy-mountkirkgames-rev2

Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. The company builds all of its games using some server-side integration. Historically, it has used cloud providers to lease physical servers.
Due to the unexpected popularity of some of its games, the company has had problems scaling its global audience, application servers, MySQL databases, and analytics tools.
The current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.

Solution Concept
Mountkirk Games is building a new game, which is expected to be very popular. The company plans to deploy the game’s backend on Google Compute Engine to capture streaming metrics, to run intensive analytics, to take advantage of its autoscaling server environment, and to integrate with a managed NoSQL database.

Business Requirements
- Increase to a global footprint
- Improve uptime—downtime means loss of players
- Increase efficiency of the cloud resources we use
- Reduce latency to all customers

Technical Requirements
For the game backend platform:
- Dynamically scale up or down based on game activity
- Connect to a transactional database service to manage user profiles and game state
- Store game activity in a time series database service for future analysis
- As the system scales, ensure that data is not lost due to processing backlogs
- Run hardened Linux distribution

For the game analytics platform:
- Dynamically scale up or down based on game activity
- Process incoming data on the fly directly from the game servers
- Process data that arrives late because of slow mobile networks
- Allow queries to access at least 10TB of historical data
- Process files that are regularly uploaded by users’ mobile devices

Executive Statement
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game’s reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users. Additionally, our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling and low-latency load balancing and frees us up from managing physical servers.

2. Dress4Win
https://cloud.google.com/certification/guides/cloud-architect/casestudy-dress4win-rev2

Dress4Win is a web-based company that helps its users organize and manage their personal wardrobes using a web app and mobile application. The company also cultivates an active social network that connects its users with designers and retailers. The company monetizes its services through advertising, e-commerce, referrals, and a “freemium” app model. The application has grown from a few servers in the founder’s garage to several hundred servers and appliances in a colocated data center. However, the capacity of the infrastructure is now insufficient for the application’s rapid growth. Because of this growth and the company’s desire to innovate faster, Dress4Win is committing to a full migration to a public cloud.

Solution Concept
For the first phase of its migration to the cloud, Dress4Win is moving its development and test environments. It is also building a disaster recovery site, because its current infrastructure is at a single location. The company is not sure which components of its architecture can be migrated as is and which components need to be changed before migrating them.

Existing Technical Environment
The Dress4Win application is served out of a single data center location. All servers run Ubuntu LTS v16.04.
- Databases MySQL: one server for user data, inventory, and static data
- MySQL 5.7
- 8 core CPUs
- 128GB of RAM
- 2× 5TB HDD (RAID 1)
- Compute Forty web application servers providing microservices-based APIs and static content
- Tomcat – Java
- Nginx
- 4 core CPUs
- 32GB of RAM

Twenty Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
- 8 core CPUs
- 128GB of RAM
- 4× 5TB HDD (RAID 1)

Three RabbitMQ servers for messaging, social notifications, and events:
- 8 core CPUs
- 32GB of RAM

Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM

Storage appliances:
- iSCSI for VM hosts
- Fibre Channel SAN: MySQL databases (1PB total storage; 400TB available)
- NAS: image storage, logs, backups (100TB total storage; 35TB available)

Business Requirements
- Build a reliable and reproducible environment with scaled parity of production
- Improve security by defining and adhering to a set of security and identity and access management (IAM) best practices for the cloud
- Improve business agility and speed of innovation through rapid provisioning of new resources
- Analyze and optimize architecture for performance in the cloud

Technical Requirements
- Easily create nonproduction environments in the cloud
- Implement an automation framework for provisioning resources in cloud
- Implement a continuous deployment process for deploying applications to the on-premises data center or cloud
- Support failover of the production environment to the cloud during an emergency
- Encrypt data on the wire and at rest
- Support multiple private connections between the production data center and cloud environment

Executive Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80 percent of our capacity is sitting idle. Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next five years for a public cloud strategy achieves a cost reduction of between 30 percent and 50 percent over our current model.

3. TerramEarth
https://cloud.google.com/certification/guides/cloud-architect/casestudy-terramearth-rev2
TerramEarth manufactures heavy equipment for the mining and agricultural industries. About 80 percent of its business comes from mining and 20 percent is from agriculture. With more than 500 dealers and service centers in 100 countries, TerramEarth’s mission is to build products that make its customers more productive.

Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, enabling the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, enabling TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day, TerramEarth collects a total of about 9TB per day from these connected vehicles.

Existing Technical Environment
TerramEarth’s existing architecture is composed of Linux- and Windows-based systems that reside in a single U.S. West Coast–based data center. These systems gzip CSV files from the field, upload via FTP, and place the data in a data warehouse. Because this process takes time, aggregated reports are based on data that is three weeks old. With this data, TerramEarth has been able to stock replacement parts preemptively and reduce unplanned downtime of its vehicles by 60 percent. However, because the data is stale, some customers are without their vehicles for up to four weeks while they wait for replacement parts.

Business Requirements
- Decrease unplanned vehicle downtime to less than one week
- Support the dealer network with more data on how their customers use their equipment to better position new products and services
- Have the ability to partner with different companies—especially with seed and fertilizer suppliers in the fast-growing agricultural business—to create compelling joint offerings for their customers

Technical Requirements
- Expand beyond a single data center to decrease latency to the American Midwest and East Coast
- Create a backup strategy
- Increase security of data transfer from equipment to the data center
- Improve data in the data warehouse
- Use customer and equipment data to anticipate customer needs

Application 1: Data Ingest
A custom Python application reads uploaded data files from a single server and writes to the data warehouse.
- Compute Windows Server 2008 R2
- 16 CPUs
- 128GB of RAM
- 10TB local HDD storage

Application 2: Reporting
Business analysts use an off-the-shelf application to run a daily report to see what equipment needs repair. Only two analysts of a team of ten (five West Coast, five East Coast) can connect to the reporting application at a time.
- Compute Off-the-shelf application; license tied to number of physical CPUs
- Windows Server 2008 R2
- 16 CPUs
- 32GB of RAM
- 500GB HDD
- Data warehouse Single PostgreSQL server
- Red Hat Linux
- 64 CPUs
- 128GB of RAM
- 4× 6TB HDD in RAID 0

Executive Statement
Our competitive advantage has always been in our manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I’m concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.