Getting Started with Stream Processing Using Apache Flink

Flink is a stateful, tolerant, and large scale system with excellent latency and throughput characteristics. It works with bounded and unbounded datasets using the same underlying stream-first architecture, focusing on streaming or unbounded data.

Apache Flink is a distributed computing engine used to process large scale data. Flink is built on the concept of stream-first architecture where the stream is the source of truth. This course, Getting Started with Stream Processing Using Apache Flink, walks the users through exploratory data analysis and data munging with Flink. You'll start off learning about simple data transformations on streams such as map(), filter(), flatMap(), reduce(), sum(), min(), and max() on simple DataStreams and KeyedStreams. You'll then learn about window transformations in detail using tumbling, sliding, count, and session windows. You'll wrap up the course explore operations on multiple streams such as union and joins. All of this with hands on demos using Flink's Java API along with a real world project using Twitter's streaming API. After you've watched this course you'll have a strong foundation for stream processing concepts using Apache Flink.

• Course Overview 1min
• Understanding Streaming Data and Stream Processing 33mins
• Implementing Basic Operations on Streaming Data 41mins
• Windowing Operations on Streams 42mins
• Fault Tolerance with State and Checkpoints 33mins
• Working with Multiple Stream Sources 11mins