English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 06m | 215 MB
Frameworks such as Apache Flink can help you build fast, scalable stream processing applications, but big data engineers still need to design smart use cases to achieve maximum efficiency. In this course, instructor Kumaran Ponnambalam demonstrates how to use Apache Flink and associated technologies to build stream-processing use cases leveraging popular patterns. Kumaran begins by highlighting the opportunities and challenges that stream processing brings to big data. He then goes over four popular patterns for stream processing: streaming analytics, alerts and thresholds, leaderboards, and real-time predictions. Along the way, he reviews example use cases and explains how to leverage Flink, as well as key technologies like MariaDB and Redis, to implement key examples.
Table of Contents
1 Stream processing with Flink
2 What you should know
3 What is stream processing
4 Streaming Opportunities and challenges
5 Streaming with Flink
6 Setting up the exercise files
7 Setting up Kafka
8 Setting up MariaDB and Redis
9 Streaming analytics Pattern
10 Streaming analytics Use case design
11 Streaming analytics Helper classes
12 Streaming analytics Pipeline implementation
13 Streaming analytics Results review
14 Alerts and thresholds Pattern
15 Alerts and thresholds Use case design
16 Alerts and thresholds Helper classes
17 Alerts and thresholds Pipeline implementation
18 Alerts and thresholds Review
19 Leaderboards Pattern
20 Leaderboards Use case design
21 Leaderboards Helper classes
22 Leaderboards Pipeline implementation
23 Leaderboards Review
24 Real-time predictions Pattern
25 Real-time predictions Use case design
26 Real-time predictions Helper classes
27 Real-time predictions Pipeline implementation
28 Real-time predictions Review
29 Use case definition
30 Design of the project
31 Code walkthrough
32 Execute and analyze
33 Next steps
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