The Ultimate Kafka Streams (3.x) : Real-time Data Processing

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Course content introduction

Lecture 2: Course objective and scope

Lecture 3: Course Characteristics & how to learning (suggestions)

Lecture 4: Download the source code & PowerPoint

Chapter 2: Apache Kafka Streams Introduction

Lecture 1: What is Kafka Streams

Lecture 2: Kafka Streams Key Terms

Lecture 3: Kafka Streams Parallel mode

Lecture 4: Data process strategy in Kafka Streams

Lecture 5: Kafka Streams Development Environment Setup(Kafka Broker Setup)

Lecture 6: Kafka Streams Development Environment setup(Maven Project)

Lecture 7: Quick Start-Develop First Kafka Streams Application

Lecture 8: Setup the First Kafka Stream Application and Testing

Chapter 3: Build Kafka Stateless Streams Application

Lecture 1: What is stateless streams application

Lecture 2: Stateless Operation- Map & KeyValueMapper

Lecture 3: Stateless Operation- MapValues & ValueMapper & ValueMapperWithKey

Lecture 4: Stateless Operation- Filter & FilterNot & Predicate

Lecture 5: Stateless Operation-FlatMap & KeyValueMapper

Lecture 6: Stateless Operation-FlatMapValues&ValueMapper&ValueMapperWithKey

Lecture 7: Stateless Operation-SelectKey & KeyValueMapper

Lecture 8: Stateless Operation-Foreach & ForeachAction

Lecture 9: Stateless Operation-Print and Peek

Lecture 10: Stateless Operation-(BranchedKStream)Split Streams to multiple sub-streams

Lecture 11: Stateless Operation-Merge Small KStreams to big one

Lecture 12: Stateless Operation-Sink the transformed records to target topic

Lecture 13: Assemble Together: XMall Transaction data real-time analysis practise

Lecture 14: Assemble Together: XMall Transaction data real-time analysis – Data Model Define

Lecture 15: Assemble Together: XMall Transaction data real-time analysis-Custom Serdes

Lecture 16: Assemble Together: XMall Transaction data real-time analysis-Streams Application

Lecture 17: Assemble Together: XMall Transaction data real-time analysis-Deploy & Testing

Lecture 18: Kafka Stateless Streams Recap & Summarize

Chapter 4: Build Kafka Stateful Streams application & statestore(RocksDB)

Lecture 1: What is stateful streams application

Lecture 2: State store & state store solution

Lecture 3: Stateful transformation practical (Word Count)

Lecture 4: Stateful transformation practical(Word Count) test and result verify

Lecture 5: Understand the Kafka streams internal data redistribution and stateful transform

Lecture 6: Enhancement XMall transaction reward point processor support total reward points

Lecture 7: Stateful transform operations

Lecture 8: KStream Joining operation introduction

Lecture 9: KStream inner joining operation

Lecture 10: KStream inner joining operation application startup & testing

Lecture 11: Depth understand the joining operation and underlying change logs

Lecture 12: KStream left joining operation

Lecture 13: KStream outer joining operation

Lecture 14: KStream grouping operation

Lecture 15: KGroupedStream count aggregation operation practical(word count)

Lecture 16: KGroupedStream reduce aggregation operation practical(word count)

Lecture 17: Real-Time analysis the sales champion application by reduce aggregate operation

Lecture 18: Develop the sales champion application by reduce aggregating operation and test

Lecture 19: KGroupedStream aggregate operation for Real-Time compute sales stats reporter

Lecture 20: Stateful KStream Queryable Storestore(KeyValueStore)-single instance

Lecture 21: Stateful KStream Queryable Storestore(KeyValueStore)-multiple instance

Lecture 22: Stateful process and ProcessorSupplier(Official documentation has bugs)

Lecture 23: Kafka Stateful Streams Recap & Summarize

Chapter 5: Build complex streaming application with KGroupedStream windowing operation

Lecture 1: Kafka Streams Time Semantics

Lecture 2: Kafka LogAppendTime&CreateTime

Lecture 3: Kafka Streams TimestampExtractor

Lecture 4: The Kafka Streams Windowing Operation introduction

Lecture 5: Tumbling time window analyzes network attack behavior in real time

Lecture 6: Tumbling time window real-time analysis of network attack behavior app building

Lecture 7: Suppress some updates from this changelog stream

Lecture 8: Hopping time window for real-time statistics the website access traffic

Lecture 9: Sliding time window for real-time statistics the website access traffic

Lecture 10: Hopping time window vs Sliding time window

Lecture 11: Session time window for real-time statistics website PV&UV

Lecture 12: Assembly-Heartbeat sensor data real-time analysis patient health monitoring-I

Lecture 13: Assembly-Heartbeat sensor data real-time analysis patient health monitoring-II

Lecture 14: Assembly-Heartbeat sensor data real-time analysis patient health monitoring-III

Lecture 15: KGroupedStream Interactive Queryable Storestore(WindowStore)

Lecture 16: KGroupedStream & Windowing Aggregation Recap and Summarize

Chapter 6: Build Updatable Streams Application with KTable

Lecture 1: What is KTable

Lecture 2: Create KTable from StreamsBuilder(Official documentation has bugs)

Lecture 3: Create KTable from KStream

Lecture 4: KTable basic operation introduction and Tombstone records explanation

Lecture 5: Lab: KTable basic operation exercises(contains tombstone records)

Lecture 6: KTable transformValues operation and shoot game

Lecture 7: KStream inner join the KTable

Lecture 8: KStream left join the KTable

Lecture 9: KTable inner join the KTable

Lecture 10: KTable Foreign Key inner join the KTable

Lecture 11: KTable supported joining operations

Lecture 12: KTable grouping & count aggregating count the number of employees

Lecture 13: KTable grouping & reduce aggregating statistics department total salary

Lecture 14: KTable grouping & aggregate aggregating statistics department total salary

Lecture 15: KTable Recap and Summarize

Chapter 7: GlobalKTable API

Lecture 1: What is GlobalKTable

Lecture 2: GlobalKTable vs KTable

Lecture 3: KStream joining(inner&left) GlobalKTable