English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 11m | 189 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
Table of Contents
1 Real-time processing and analytics
2 What is Apache Flink
3 Streaming with Apache Flink
4 DataStream API
5 Related prerequisite courses
6 Setting up exercise files
7 Setting up the Flink environment
8 Reading from a stream source
9 Processing streaming data
10 Writing to a stream sink
11 Using keyed streams
12 ProcessFunction
13 Splitting a stream
14 Merging multiple streams
15 Windowing concepts
16 Using a Kafka streaming source
17 Using sliding windows
18 Using session windows
19 Window joins
20 Time attributes in Flink
21 Watermarks
22 Setting up event time
23 Processing with event time
24 Writing to a Kafka sink
25 State management in Flink
26 Defining states
27 Using states
28 Advanced state management
29 Problem definition
30 Computing summary counts
31 Computing activity durations
32 Next steps
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