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.
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: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.Race predictions are performed using TensorFlow running on VMs 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.Increase predictive capabilities during and before races: -‹ Race results -‹ Mechanical failures -‹ Crowd sentimentIncrease 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.
Executive statement -
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A. B. C. D.C.
https://cloud.google.com/bigquery/public-dataThe solution concept in this scenario is to migrate the existing service of Helicopter Racing League (HRL) 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.
The existing technical environment of HRL is a public cloud-first company, and the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing are performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. The race prediction services are hosted exclusively on their existing public cloud provider. 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. Race predictions are performed using TensorFlow running on VMs in the current public cloud provider. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers.
The business requirements of HRL owners are to expand their predictive capabilities and reduce latency for their viewers in emerging markets. They require support for the ability to expose the predictive models to partners, increase predictive capabilities during and before races, 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, and create a merchandising revenue stream.
The technical requirements are to maintain or increase prediction throughput and accuracy, reduce viewer latency, increase transcoding performance, create real-time analytics of viewer consumption patterns and engagement, and create a data mart to enable processing of large volumes of race data.
Option A suggests using Firestore for its scalable and flexible document-based database. Collections can be used to aggregate race data by season and event. Firestore is a NoSQL document database that can store and sync data for client and server-side development. It provides real-time updates and automatic scaling, making it an ideal solution for HRL to manage and store their race data.
Option B suggests using Cloud Spanner for its scalability and ability to version schemas with zero downtime. Race data can be split using season as a primary key. Cloud Spanner is a horizontally scalable, strongly consistent, relational database service that can handle global data distribution with automatic synchronous replication and transactional consistency. It can handle high write and read loads, and support high availability and reliability.
Option C suggests using BigQuery for its scalability and ability to add columns to a schema. Race data can be partitioned based on season. BigQuery is a serverless, highly scalable, and cost-effective data warehouse designed for processing and analyzing large and complex datasets. It can handle structured and semi-structured data with SQL-like queries and machine learning integrations.
Option D suggests using Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Separate database instances can be used for each season. Cloud SQL is a fully managed relational database service that supports MySQL, PostgreSQL, and SQL Server. It provides automatic scaling, backup, and replication features with easy integration into other Google Cloud services.
Considering the business and technical requirements of HRL, Option C is the best solution for migrating their existing service to a new platform. BigQuery can handle large volumes of race data, and partitioning by season can improve query performance. BigQuery's machine learning integrations can enhance predictive capabilities, and its real-time analytics features can provide insights into viewer engagement and consumption patterns. BigQuery's scalability and cost-effectiveness can meet HRL's requirements for global availability and quality of the broadcasts, while minimizing operational complexity and ensuring compliance with regulations. Additionally, HRL can create a data mart in BigQuery to enable processing of large volumes of race data