MP4 | Video: AVC, 1280x720 30 fps | Audio: AAC, 48 KHz, 2 Ch | Duration: 1h 11m
Skill Level: Advanced | Genre: eLearning | Language: English + Subtitles | Size: 187 MB

From an engineering perspective, scalability is one of the most pressing challenges in data science. Apache Flink, the powerful and popular stream-processing platform, offers features and functionality that can help developers tackle this challenge. In this course, learn how to build a real-time stream processing pipeline with Apache Flink. Instructor Kumaran Ponnambalam begins by reviewing key streaming concepts and features of Apache Flink. He then takes a deeper look at the DataStream API and explores various capabilities available for real-time stream processing, including windowing and joins. After delving into the platform's event-time processing and state management features, he provides a use case project that allows you to put your new skills to the test.

Topics include:

Streaming with Apache Flink
Using the DataStream API for basic stream processing
Working with process functions
Windowing and joins
Setting up event-time processing
State management in Flink
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