Latest Helicopter Racing League Case Study (2024) - Google Cloud (GCP) Architect

We will be sharing our analysis for GCP Professional Cloud Architect - Helicopter Racing League is a GCP Master Case Study for IoT to help you in your study and exam preparation.

This case study has been around for a while and was recently modified on May 1, 2021 to coincide with the latest version of the Architect certificate exam. Let's discuss understand and analyze this case study

Company overview

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.

Key points here are :

• They have a world championship and several regional competitions every year.
• They offer paid streaming service for raises all around the world with live telemetry, live metrics and life predictions.

Solution concept

HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users

Existing technical environment

HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:

Key points here are :

• They are making use of an existing public cloud provider, something other than Google Cloud.
• In that provider, they are hosting a number of their mission critical apps.
• They have a lot of existing content which is stored in an object storage service.
• Race Predictions are performed using TensorFlow running on VMs which allow prediction however they lack capabilities like :
  • facility to support real time predictions during races.
  • They are missing is the capacity to process season long results. They would want to be able to look at the results from the entire season and generate analysis based on that.
• On the racetracks, video filming and editing are done. The content is encoded and sent to the cloud using custom-built tools. When you're streaming video, you'll often have various views on different platforms, which you'll want to be able to encode and transfer. That is why the content is encoded in transcripted format.
• Truck-mounted mobile data centres are also being used to provide enterprise, great connection, and local compute. So while a race is taking place, they have a track-mounted mobile data centre ready to go, and this is how they provide high-speed connectivity to their on-premises customers.

Business requirements

HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:

• Support ability to expose the predictive models to partners.
• Increase predictive capabilities during and before races:
  • Race results
  • Mechanical failures
  • Crowd sentiment
• Increase telemetry and create additional insights.
• Measure fan engagement with new predictions.
• Enhance global availability and quality of the broadcasts.
• Increase the number of concurrent viewers.
• Minimize operational complexity.
• Ensure compliance with regulations.
• Create a merchandising revenue stream.

Technical requirements

• Maintain or increase prediction throughput and accuracy
• Reduce viewer latency
• Increase transcoding performance.
• Create real-time analytics of viewer consumption patterns and engagement
• Create a data mart to enable processing of large volumes of race data.

Key points here are :

• Migrate existing services to a new platform and the new platform:
  • Expand the use of managed AI and ML services to facilitate race predictions.
    Increase the predictive capabilities during and before races :
    • want ability to predict mechanical failure and how the crowd is reacting to the race.
    • want ability to enhanced video screens that include predictions of the event within the race.
    • Might want to predict important event like overtaking .
    • Want to maintain or increase the prediction, throughput and accuracy
  • Expose the predictive models to partners
  • Want real time analytics of your consumption patterns and engagement.
• Director of requirements are related to moving serving of the real time and recorded content closer to their users:
  • reduce latency, enhance global availability and the quality of the of their broadcasts.
  • increase the number of concurrent viewers
• want to create a data market. Data market is nothing but a data warehouse where you can actually store huge volumes of data.:
• increase transcoding performance as well

Executive statement

Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.

Key points here are :

5 year strategic plan :
• Want to create a partner ecosystem of new products by enabling access to their data
• More autonomous operations you would want, the more you would want to go to realtime streaming.
• Move legacy systems to the cloud

Reference Architecture Solution diagram

Below architecture depicts different aspect like real-time, batch flow, CI-CD (Cloud Build, Spinnaker, Jenkins), Apigee, Secret Management (KMS, Cloud IAM), GKE, Cloud Monitoring and logging :