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Flink and Streaming Fundamentals
You'll understand how Flink handles stream processing, distributed and stateful computation;
You'll learn Flink's architecture including Flink cluster's components;
You'll know how to deploy and manage the lifecycle of a Flink application
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Data Pipeline
You'll understand different levels of abstraction for developing streaming applications;
You'll be able to process big data in real time any way you want to by mastering fundamental Flink concepts including: data ingestion, efficient data transformation, controlling your applications with lower level APIs, producing output streams to data sinks
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Integration with Apache Kafka
You'll learn configuration of Kafka Source and Kafka Sink; You'll master how to set up Kafka dependencies in built.sbt and how to integrate Kafka with Flink as a data source or data sink
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Time Handling, Watermarks and Windows
You'll be able to handle event time processing using Flink's watermarks mechanism and window operation including tumbling window, sliding window and global window
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Fault Tolerant
You'll be able to write stateful applications using Flink's key concepts including checkpoint mechanism, map state, list state and value state
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Integration with Kubernetes
You'll learn Flink's deployment modes and deploy your own Flink application on Kubernetes by following along the video demonstration of every deployment step and deployment configurations