Google Cloud – EHR Healthcare Case Study

Google Cloud – EHR Healthcare Case Study

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

Executive statement

Our on-premises strategy has worked for years but has required a major investment of time and money in training our team on distinctly different systems, managing similar but separate environments, and responding to outages. Many of these outages have been a result of misconfigured systems, inadequate capacity to manage spikes in traffic, and inconsistent monitoring practices. We want to use Google Cloud to leverage a scalable, resilient platform that can span multiple environments seamlessly and provide a consistent and stable user experience that positions us for future growth.

EHR Healthcare wants to move to Google Cloud to expand, build scalable and highly available applications. It also wants to leverage automation and IaaC to provide consistency across environments and reduce provisioning errors.

Solution Concept

Due to rapid changes in the healthcare and insurance industry, EHR Healthcare’s business has been growing exponentially year over year. They need to be able to scale their environment, adapt their disaster recovery plan, and roll out new continuous deployment capabilities to update their software at a fast pace. Google Cloud has been chosen to replace its current colocation facilities.

EHR wants to build a scalable, Highly Available, Disaster Recovery setup and introduce Continous Integration and Deployment in their setup.

Existing Technical Environment

EHR’s software is currently hosted in multiple colocation facilities. The lease on one of the data centers is about to expire.
Customer-facing applications are web-based, and many have recently been containerized to run on a group of Kubernetes clusters. Data is stored in a mixture of relational and NoSQL databases (MySQL, MS SQL Server, Redis, and MongoDB).
EHR is hosting several legacy file- and API-based integrations with insurance providers on-premises. These systems are scheduled to be replaced over the next several years. There is no plan to upgrade or move these systems at the current time.
Users are managed via Microsoft Active Directory. Monitoring is currently being done via various open-source tools. Alerts are sent via email and are often ignored.

  • As the lease of one of the data centers is about to expire, time is critical
  • Some web applications are containerized and have SQL and NoSQL databases and can be moved
  • Some of the systems are legacy and would be replaced and need not be migrated
  • Team has multiple monitoring tools and might need consolidation

Business requirements

On-board new insurance providers as quickly as possible.

Provide a minimum 99.9% availability for all customer-facing systems.

  • Availability can be increased by hosting applications across multiple zones and using managed services which span multiple AZs

Provide centralized visibility and proactive action on system performance and usage.

  • Cloud Monitoring can be used to provide centralized visibility and alerting can provide proactive action
  • Cloud Logging can be also used for log monitoring and alerting

Increase ability to provide insights into healthcare trends.

  • Data can be pushed and analyzed using BigQuery and insights visualized using Data studio.

Reduce latency to all customers.

  • Performance can be improved using Global Load Balancer to expose the applications.
  • Applications can also be hosted across regions for low latency access.

Maintain regulatory compliance.

  • Regulatory compliance can be maintained using data localization, data retention policies as well as security measures.

Decrease infrastructure administration costs.

  • Infrastructure administration costs can be reduced using automation with either Terraform or Deployment Manager

Make predictions and generate reports on industry trends based on provider data.

  • Data can be pushed and analyzed using BigQuery.

Technical requirements

Maintain legacy interfaces to insurance providers with connectivity to both on-premises systems and cloud providers.

Provide a consistent way to manage customer-facing applications that are container-based.

  • Containers based applications can be deployed GKE or Cloud Run with consistent CI/CD experience

Provide a secure and high-performance connection between on-premises systems and Google Cloud.

  • Cloud VPN, Dedicated Interconnect, or Partner Interconnect connections can be established between on-premises and Google Cloud

Provide consistent logging, log retention, monitoring, and alerting capabilities.

Maintain and manage multiple container-based environments.

  • Use Deployment Manager or IaaC to provide consistent implementations across environments

Dynamically scale and provision new environments.

  • Applications deployed on GKE can be scaled using Cluster Autoscaler and HPA for deployments.

Create interfaces to ingest and process data from new providers.

GCP Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • GCP services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • GCP exam questions are not updated to keep up the pace with GCP updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. For this question, refer to the EHR Healthcare case study. In the past, configuration errors put IP addresses on backend servers that should not have been accessible from the internet. You need to ensure that no one can put external IP addresses on backend Compute Engine instances and that external IP addresses can only be configured on the front end Compute Engine instances. What should you do?
    1. Create an organizational policy with a constraint to allow external IP addresses on the front end Compute Engine instances
    2. Revoke the compute.networkadmin role from all users in the project with front end instances
    3. Create an Identity and Access Management (IAM) policy that maps the IT staff to the compute.networkadmin role for the organization
    4. Create a custom Identity and Access Management (IAM) role named GCE_FRONTEND with the compute.addresses.create permission

EHR Healthcare References

EHR Healthcare Case Study

Google Cloud – TerramEarth Case Study

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.

Key points here are 500 dealers and service centers are spread across the world and they want to 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

Key points here are TerramEarth has 2 million vehicles. Only critical data is transferred in real-time while the rest of the data is uploaded in bulk daily.

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.

Key point here is the company wants to improve further in operations, customer experience, and partner ecosystem by allowing them to reuse the data.

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 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.

Key point here is the company is hosting its infrastructure in Google Cloud and private data centers. GCP has web frontend and vehicle data aggregation & analysis. Data is sent to private data centers.

Business Requirements

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

  • Cloud IoT core can provide a fully managed service to easily and securely connect, manage, and ingest data from globally dispersed devices.
  • Existing legacy inventory and logistics management systems running in the private data centers can be migrated to Google Cloud.
  • Existing data can be migrated one time using Transfer Appliance.

Decrease cloud operational costs and adapt to seasonality.

    • Google Cloud provides configuring elasticity and scalability for resources based on the demand.

Increase speed and reliability of development workflow.

    • Google Cloud CI/CD tools like Cloud Build and open-source tools like Spinnaker can be used to increase the speed and reliability of the deployments.

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

  • Cloud Function to Function authentication

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

  • Google Cloud provides multiple fully managed serverless and scalable application hosting solutions like Cloud Run and Cloud Functions
  • Managed Instance group with Compute Engines and GKE cluster with scaling can also be used to provide scalable, highly available compute services.

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.

    • Google Cloud API Gateway & Cloud Endpoints can be used to provide an abstraction layer to expose the data externally over a variety of backends.

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

Google Cloud CI/CD - Continuous Integration Continuous Deployment

    • Google Cloud provides DevOps tools like Cloud Build and supports open-source tools like Spinnaker to provide CI/CD features.
    • Cloud Source Repositories are fully-featured, private Git repositories hosted on Google Cloud.
    • Cloud Build is a fully-managed, serverless service that executes builds on Google Cloud Platform’s infrastructure.
    • Container Registry is a private container image registry that supports Docker Image Manifest V2 and OCI image formats.
    • Artifact Registry is a fully-managed service with support for both container images and non-container artifacts, Artifact Registry extends the capabilities of Container Registry.

Allow developers to run experiments without compromising security and governance requirements

    • Google Cloud deployments can be configured for Canary or A/B testing to allow experimentation.

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.

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

    • Google Cloud supports Key Management Service – KMS and Secrets Manager for managing secrets and key management.

Improve and standardize tools necessary for application and network monitoring and troubleshooting.

    • Google Cloud provides Cloud Operations Suite which includes Cloud Monitoring and Logging to cover both on-premises and Cloud resources.
    • Cloud Monitoring collects measurements of key aspects of the service and of the Google Cloud resources used
    • Cloud Monitoring Uptime check is a request sent to a publicly accessible IP address on a resource to see whether it responds.
    • Cloud Logging is a service for storing, viewing, and interacting with logs.
    • Error Reporting aggregates and displays errors produced in the running cloud services.
    • Cloud Profiler helps with continuous CPU, heap, and other parameters profiling to improve performance and reduce costs.
    • Cloud Trace is a distributed tracing system that collects latency data from the applications and displays it in the Google Cloud Console.
    • Cloud Debugger helps inspect the state of an application, at any code location, without stopping or slowing down the running app.


Reference Cellular Upload Architecture

Batch Upload Replacement Architecture


Google Cloud – Mountkirk Games Case Study

Google Cloud – Mountkirk Games Case Study

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. Their most recent endeavor is to create a retro-style first-person shooter (FPS) game that allows hundreds of simultaneous players to join a geo-specific digital arena from multiple platforms and locations. A real-time digital banner will display a global leaderboard of all the top players across every active arena.

Solution Concept

Mountkirk Games is building a new multiplayer game that they expect to be very popular. They plan to deploy the game’s backend on Google Kubernetes Engine so they can scale rapidly and use Google’s global load balancer to route players to the closest regional game arenas. In order to keep the global leader board in sync, they plan to use a multi-region Spanner cluster.

So the key here is the company wants to deploy the new game to Google Kubernetes Engine exposed globally using a Global Load Balancer and configured to scale rapidly and bring it closer to the users. Backend DB would be managed using a multi-region Cloud Spanner cluster.

Executive Statement

Our last game was the first time we used Google Cloud, and it was a tremendous success. We were able to analyze player behavior and game telemetry in ways that we never could before. This success allowed us to bet on a full migration to the cloud and to start building all-new games using cloud-native design principles. Our new game is our most ambitious to date and will open up doors for us to support more gaming platforms beyond mobile. Latency is our top priority, although cost management is the next most important challenge. As with our first cloud-based game, we have grown to expect the cloud to enable advanced analytics capabilities so we can rapidly iterate on our deployments of bug fixes and new functionality.

So the key points here are the company has moved to Google Cloud with great success and wants to build new games in the cloud. Key priorities are high performance, low latency, cost, advanced analytics, quick deployment, and time-to-market cycles.

Business Requirements

Support multiple gaming platforms.

Support multiple regions.

Support rapid iteration of game features.

  • Can be handled using Deployment Manager and IaaC services like Terraform to automate infrastructure provisioning
  • Cloud Build + Cloud Deploy/Spinnaker can be used for rapid continuous integration and deployment

Minimize latency

  • can be reduced using a Global HTTP load balancer, which would route the user to the closest region
  • using multi-regional resources like Cloud Spanner would also help reduce latency

Optimize for dynamic scaling

  • can be done using GKE Cluster Autoscaler and Horizontal Pod Autoscaling to dynamically scale the nodes and applications as per the demand
  • Cloud Spanner can be scaled dynamically

Use managed services and pooled resources.

  • Using GKE, with Global Load Balancer for computing and Cloud Spanner would help cover the application stack using managed services

Minimize costs.

  • Using minimal resources and enabling auto-scaling as per the demand would help minimize costs

Existing Technical Environment

The existing environment was recently migrated to Google Cloud, and five games came across using lift-and-shift virtual machine migrations, with a few minor exceptions. Each new game exists in an isolated Google Cloud project nested below a folder that maintains most of the permissions and network policies. Legacy games with low traffic have been consolidated into a single project. There are also separate environments for development and testing.

Key points here are the resource hierarchy exists with a project for each new game under a folder to control access using Service Control Permissions. Also, some of the small games would be hosted in a single project. There are also different environments for development, testing, and production.

Technical Requirements

Dynamically scale based on game activity.

  • can be done using GKE Cluster Autoscaler and Horizontal Pod Autoscaling to dynamically scale the nodes and applications as per the demand

Publish scoring data on a near-real-time global leaderboard.

  • can be handled using Pub/Sub for capturing data and Cloud DataFlow for processing the data on the fly i.e real time

Store game activity logs in structured files for future analysis.

  • can be handled using Cloud Storage to store logs for future analysis
  • analysis can be handled using BigQuery either loading the data or using federated data source
  • data can also be stored directly using BigQuery as it would provide a low-cost data storage (as compared to Bigtable) for analytics 
  • another advantage of BigQuery over Bigtable in this case its multi-regional, meeting the global footprint and latency requirements

Use GPU processing to render graphics server-side for multi-platform support.

  • Support eventual migration of legacy games to this new platform.

Reference Architecture

Mobile Gaming Analysis Telemetry Solution

Refer to Mobile Gaming Analysis Telemetry solution

Mobile Gaming Analysis Telemetry Solution

Mountkirk Games References