Changing Parameters for A/B Testing, with Apache Flink | 4/5

Discover how the control channel method can help you change application parameters and filters to perform A/B testing and feature switching, with zero downtime.  In this series, Anand Shah (Data Analytics and Streaming Specialist at AWS) will help you build a modern data streaming architecture for a real-time gaming leaderboard. This architecture includes data ingestion, real-time enrichment with database change data capture (CDC), data processing, as well as computing, storing and visualizing the results. You will also learn advanced streaming analytics techniques, such as the control channel method for A/B testing, updating features and parameters with zero downtime, and how to handle late arrival of data. Anand will also talk you through the process of data de-duplication, as well as how you can store historical data for replay on-demand. 🎉 🌟 Get started with Amazon Managed Service for Apache Flink today, to build and run your fully managed Apache Flink applications on AWS! 🔗 Github repository: https://github.com/build-on-aws/real-time-gaming-leaderboard-apache-flink Resources used in this video: 🔗 Intro to Kinesis Data Streams: https://docs.aws.amazon.com/streams/latest/dev/introduction.html Follow AWS Developers: 👾 Twitch: https://twitch.tv/aws 🐦 Twitter: https://twitter.com/awsdevelopers 💻 LinkedIn: https://www.linkedin.com/showcase/aws Follow Anand Shah:  🐦 Twitter: https://twitter.com/anandshah110 💻 LinkedIn: https://www.linkedin.com/in/anandshah110/ 00:00 Intro 00:20 What will you learn? 00:51 Control channel technique - Architecture 01:12 Control channel technique - Deep dive 02:20 Control channel technique - Use cases 02:50 Demo: CDK source code walkthrough and deployment 04:01 Demo: Applying control channel technique in Managed Flink Notebooks 05:23 Demo: Challenge 4 – Dynamic bot filtering and A/B testing 06:01 Demo: Push config events using AWS Lambda 07:39 Conclusion  #ZeroDownTime, #DynamicConfigurations, #M