Case Study Review: Helicopter Racing League
Credit goes to Indro Bhattacharya & countless Googlers for this series of case study posts
As most of you know by now, the Google PCA (Professional Cloud Architect) exam was revamped on May 1st, 2021. With the new version of the exam, and having cleared it myself last month, I noticed some significant changes. Some of the key changes from the previous version of the exam are:
The questions are more conceptual than straightforward
Introduction of new areas like Anthos and MLOps
Longer questions
Multiple services being tested on a question (like a true architect!)
All new case studies
In this blog post, I will outline how I went about solving the new case studies. I will post the exact document I wrote, and which since May 14th 2021, over 240 Googlers across the globe have used as part of their exam prep. I want to thank the many Googlers who took time to comment and improve the document to get it to its current state. Big shout out to Iman for allowing me to post this on his amazing website. I hope this material will help in your prep as well.
If you haven’t already, please read the exam deep dive to understand the overall strategy and key objectives to study for the Professional Cloud Architect exam.
All the best!
Helicopter Racing League
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 (real time analytics) throughout each race.
https://www.youtube.com/watch?v=cwTiYOtmQbI&list=PLne_-oJR60mOciEvtX1AfER8lBafH8Lox&index=3’
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services (AI Platform) 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 (GCS), closer to their users (CDN) .
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:
Existing content is stored in an object storage service on their existing public cloud provider.
Video encoding and transcoding is performed on VMs created for each job (too many VMs, not sustainable)
Race predictions are performed using TensorFlow running on VMs (Custom Code, no managed services) in the current public cloud provider.
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 (Apigee).
Increase predictive capabilities during and before races: (AI Platform)
Race results
Mechanical failures
Crowd sentiment
Increase telemetry and create additional insights (IoT Core, BQ, Looker/Datastudio)
Measure fan engagement with new predictions (AI Platform)
Enhance global availability and quality of the broadcasts (CDNs)
Increase the number of concurrent viewers (CDNs, Global LB)
Minimize operational complexity (Managed services)
Ensure compliance with regulations (GDPR,PII)
Create a merchandising revenue stream (Separate API?)
Technical requirements
Maintain or increase prediction throughput and accuracy (BQ ML function can predict)
Reduce viewer latency (CDN)
Increase transcoding performance (Transcoder API)
Create real-time analytics of viewer consumption patterns and engagement (Trucks/On prem to PubSub,Dataflow,Bigtable, Looker)
Create a data mart to enable processing of large volumes of race data (GCS,Dataflow,BQ,AI Platform - for managed tensorflow VMs)- Dataplex - New offering
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.
Basic evaluation
Client
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Values
Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud.
Real time predictions to users
Immediate Goals
Large data processing (season long)
Increase fan base and provide low latency viewing experience
Increase telemetry and create additional insights
Technical evaluation
Requirements
Content Serving from regions closer to the viewer.
Technical Watchpoints
Both real time and recorded
Proposed Solution
Use GCS to store the raw and transcoded content (Multi-Region)
Use CDN to deliver content
Requirements
Datamart for batch data processing
Technical Watchpoints
Season long data (possibly petabyte scale)
Proposed Solution
GCS can be used to store the unstructured video data. After transcoding, the data can be uploaded back to GCS and then to AI Platform for predictions (Offline Mode)
Requirements
Real time analytics and predictions
Technical Watchpoints
Increased telemetry
Real time predictions and analytics
Proposed Solution
Pub/Sub and Cloud Dataflow for ingestion and processing
AI Platform can be used for modeling and real time insights generation (also an option since they are already using Tensorflow)
Requirements
Real time Video Analysis and Transcoding performance
Technical Watchpoints
Today the transcoding & encoding is done as required on the cloud
Proposed Solution
GCS to store the videos
Cloud Function is triggered each time content is uploaded
CF calls the Transcoder API
Transcoded video stored in GCS in new bucket
Video Intelligence API (for video analysis) triggered via CF
Requirements
Expose Prediction Model to Partners
Proposed Solution
Use Apigee or Cloud Endpoints to expose prediction model to partners, Apigee if it requires monetization
Requirements
Minimize Operational Complexity
Technical Watchpoints
Using trucks to stream data from race location and performing trans/encoding services on the cloud
Proposed Solution
Cloud Operations suite for SRE
Products: Cloud Operations, APigee, Video Intelligence API, Transcoder API, Cloud Functions, GCS, Pub/Sub, Dataflow, AI Platform, CDN
Stay tuned for case study reviews on each of the four business cases on the exam. In the meantime, don’t forget to check out the exam deep dive! Best of luck to you throughout your studies, you’ll do GREAT!
