Apache Kafka - Real-time Stream Processing (Master Class) - Streaming Joins

This video tutorial introduces the concept of joins in data systems, focusing on the Kafka Streams framework. It explains the types of joins available, such as inner, left outer, and full outer joins, and their specific requirements and limitations. The tutorial highlights the importance of record keys and co-partitioning for successful joins, with exceptions for global K tables. It also discusses the outcomes of different join operations and emphasizes the need for windowed joins in certain cases. The video concludes with a promise of practical examples in subsequent lectures.

What are the two categories of data sets in Kafka Streams?

• K Stream and K Table

• Batch Stream and Real-time Table

• Data Stream and Data Table

• Stream Table and Global Table

What is a mandatory requirement for parallel processing in Kafka when performing joins?

• Co-partitioning of input topics

• Using different partitioning strategies

• Using a single partition

• Having a null key

Which type of join does not require co-partitioning in Kafka Streams?

• K Stream to K Stream

• K Stream to Global K Table

• K Stream to K Table

• K Table to K Table

Which join type is not directly supported by Kafka but can be achieved by swapping data sets?

• Left Outer Join

• Inner Join

• Right Outer Join

• Full Outer Join

What is the result of a K Stream to K Stream join?

• A new K Stream

• A new Data Table

• A new Global K Table

• A new K Table