Getting Started with Stream Processing Using Apache Flink
Pluralsight
Course Summary
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.
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Course Description
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.
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Course Syllabus
Course Overview- 1m 34s
—Course Overview 1m 34sUnderstanding Streaming Data and Stream Processing- 33m 37s
—Why Stream Processing? 2m 16s
—Batch Processing vs. Stream Processing 7m 3s
—Requirements of Stream Processing Systems 5m 12s
—Micro-batches for Stream Processing 2m 17s
—Introducing Apache Flink for Stream Processing 4m 51s
—Clients, Masters, and Workers 4m 13s
—Install and Set up Flink 7m 43sImplementing Basic Operations on Streaming Data- 41m 35sWindowing Operations on Streams- 42m 45sFault Tolerance with State and Checkpoints- 33m 35sWorking with Multiple Stream Sources- 11m 37s