Oreilly – Kafka Streams in Action, Second Edition, Video Edition 2024-5

Published on: 2024-08-06 15:52:06

Categories: 28

Description

Kafka Streams in Action Second Edition Video Edition course. In this tutorial, the speaker reads the book while the content, images, code listings, diagrams, and text are displayed on the screen. Like an audiobook that you can also watch as a video. Enterprise applications need to process thousands or even millions of data events every day. With an intuitive API and impeccable reliability, the Kafka Streams style library has found a place at the center of these systems. Kafka Streams provides exactly the power and simplicity you need to manage real-time event processing or microservices messaging. This course provides everything you need to implement data stream processing on Apache KafkaⓇ using Kafka Streams and the kqsIDB event stream database. Kafka Streams in Action, Second Edition guides you through setting up and maintaining data stream processing with Kafka. In this course, you’ll find thorough coverage of not only Kafka Streams, but all the tools you need for effective streaming, including Kafka ecosystem components, producer and consumer clients, Connect, and the Schema Registry.

Kafka Streams in Action, Second Edition, teaches you how to build event-streaming applications on the amazing Apache Kafka platform. This completely revised and new version now covers a wider range of streaming architectures and includes data integration with Kafka Connect. As you progress, you’ll explore real-world examples that introduce components and brokers, data schema management, and other essentials. Along the way, you’ll learn practical techniques for combining Kafka with Spring, low-level control of processors and state stores, storing event data with ksqlDB, and testing streaming applications.

What you will learn

Designing data flow applications in Kafka Streams with KStream and API Processor
Integrating external systems with Kafka Connect
Apply data compatibility with Schema Registry
Build applications that immediately respond to events in Kafka Streams or ksqlDB
Creating materialized views on streams with ksqlDB

This course is suitable for people who

For Java developers. No prior knowledge of Kafka or data streaming applications is required.

Kafka Streams in Action Second Edition Video Edition course specifications

Publisher: Oreilly
Instructor: Bill Bejeck
Training level: beginner to advanced
Training duration: 14 hours and 11 minutes

Course headings

Part 1.
Chapter 1. Welcome to the Kafka event streaming platform
Chapter 1. What is an event?
Chapter 1. An event stream example
Chapter 1. Introducing the Apache Kafka event streaming platform
Chapter 1. A concrete example of applying the Kafka event streaming platform
Chapter 1. Summary
Chapter 2. Kafka brokers
Chapter 2. Produce requests
Chapter 2. Fetch requests
Chapter 2. Topics and partitions
Chapter 2. Sending your first messages
Chapter 2. Segments
Chapter 2. Tiered storage
Chapter 2. Cluster metadata
Chapter 2. Leaders and followers
Chapter 2. Checking for a healthy broker
Chapter 2. Summary
Part 2.
Chapter 3. Schema Registry
Chapter 3. What is a schema, and why do you need one?
Chapter 3. Subject name strategies
Chapter 3. Schema compatibility
Chapter 3. Schema references
Chapter 3. Schema references and multiple events per topic
Chapter 3. Schema Registry (de)serializers
Chapter 3. Serialization without Schema Registry
Chapter 3. Summary
Chapter 4. Kafka clients
Chapter 4. Producing records with the KafkaProducer
Chapter 4. Consuming records with the KafkaConsumer
Chapter 4. Exactly-once delivery in Kafka
Chapter 4. Using the Admin API for programmatic topic management
Chapter 4. Handling multiple event types in a single topic
Chapter 4. Summary
Chapter 5. Kafka Connect
Chapter 5. Integrating external applications into Kafka
Chapter 5. Getting started with Kafka Connect
Chapter 5. Applying Single Message Transforms
Chapter 5. Adding a sink connector
Chapter 5. Building and deploying your own connector
Chapter 5. Summary
Part 3.
Chapter 6. Developing Kafka Streams
Chapter 6. Kafka Streams DSL
Chapter 6. Hello World for Kafka Streams
Chapter 6. Masking credit card numbers and tracking purchase rewards in a retail sales setting
Chapter 6. Interactive development
Chapter 6. Choosing which events to process
Chapter 6. Summary
Chapter 7. Streams and state
Chapter 7. Adding stateful operations to Kafka Streams
Chapter 7. Stream-stream joins
Chapter 7. State stores in Kafka Streams
Chapter 7. Summary
Chapter 8. The KTable API
Chapter 8. KTables are stateful
Chapter 8. The KTable API
Chapter 8. KTable aggregations
Chapter 8. GlobalKTable
Chapter 8. Table joins
Chapter 8. Summary
Chapter 9. Windowing and timestamps
Chapter 9. Handling out order data with grace—literally
Chapter 9. Final windowed results
Chapter 9. Timestamps in Kafka Streams
Chapter 9. The TimestampExtractor
Chapter 9. Stream time
Chapter 9. Summary
Chapter 10. The Processor API
Chapter 10. Digging deeper into the Processor API with a stock analysis processor
Chapter 10. Data-driven aggregation
Chapter 10. Integrating the Processor API and the Kafka Streams API
Chapter 10. Summary
Chapter 11. ksqlDB
Chapter 11. More about streaming queries
Chapter 11. Persistent vs. push vs. pull queries
Chapter 11. Creating Streams and Tables
Chapter 11. Schema Registry integration
Chapter 11. ksqlDB advanced features
Chapter 11. Summary
Chapter 12. Spring Kafka
Chapter 12. Using Spring to build Kafka-enabled applications
Chapter 12. Spring Kafka Streams
Chapter 12. Summary
Chapter 13. Kafka Streams Interactive Queries
Chapter 13. Learning about Interactive Queries
Chapter 13. Summary
Chapter 14. Testing
Chapter 14. Summary
appendix A. Schema compatibility workshop
appendix A. Forward compatibility
appendix A. Full compatibility
appendix B. Confluent resources
appendix B. Confluent command-line interface
appendix B. Confluent local
appendix C. Working with Avro, Protobuf, and JSON Schema
appendix C. Protocol Buffers
appendix C. JSON Schema
appendix D. Understanding Kafka Streams architecture
appendix D. Consumer and producer clients in Kafka Streams
appendix D. Assigning, distributing, and processing events
appendix D. Threads in Kafka Streams: StreamThread
appendix D. Processing records