Stream Processing Patterns in Apache Flink

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Kumaran Ponnambalam

1:06:40

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01 Introduction

001 Stream processing with Flink.mp4

01:05

002 What you should know.mp4

02:16

02 Stream Processing with Flink

001 What is stream processing .mp4

02:15

002 Streaming Opportunities and challenges.mp4

02:37

003 Streaming with Flink.mp4

02:37

004 Setting up the exercise files.mp4

01:19

005 Setting up Kafka.mp4

04:04

006 Setting up MariaDB and Redis.mp4

02:36

03 Streaming Analytics

001 Streaming analytics Pattern.mp4

02:22

002 Streaming analytics Use case design.mp4

01:29

003 Streaming analytics Helper classes.mp4

02:53

004 Streaming analytics Pipeline implementation.mp4

03:33

005 Streaming analytics Results review.mp4

01:37

04 Alerts and Thresholds

001 Alerts and thresholds Pattern.mp4

02:41

002 Alerts and thresholds Use case design.mp4

01:48

003 Alerts and thresholds Helper classes.mp4

01:41

004 Alerts and thresholds Pipeline implementation.mp4

03:26

005 Alerts and thresholds Review.mp4

01:22

05 Leaderboards

001 Leaderboards Pattern.mp4

01:42

002 Leaderboards Use case design.mp4

01:49

003 Leaderboards Helper classes.mp4

02:06

004 Leaderboards Pipeline implementation.mp4

01:46

005 Leaderboards Review.mp4

01:02

06 Real-Time Predictions

001 Real-time predictions Pattern.mp4

02:38

002 Real-time predictions Use case design.mp4

01:34

003 Real-time predictions Helper classes.mp4

02:32

004 Real-time predictions Pipeline implementation.mp4

01:36

005 Real-time predictions Review.mp4

01:22

07 Use Case Project

001 Use case definition.mp4

02:00

002 Design of the project.mp4

01:00

003 Code walkthrough.mp4

02:13

004 Execute and analyze.mp4

00:57

08 Conclusion

001 Next steps.mp4

00:42

Description

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.

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Data Science

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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