Kafka Streams Basics

Anuj Saxena walks through Kafka Streams and provides a quick example:

The features provided by Kafka Streams:

  • Highly scalable, elastic, distributed, and fault-tolerant application.

  • Stateful and stateless processing.

  • Event-time processing with windowing, joins, and aggregations.

  • We can use the already-defined most common transformation operation using Kafka Streams DSL or the lower-level processor API, which allow us to define and connect custom processors.

  • Low barrier to entry, which means it does not take much configuration and setup to run a small scale trial of stream processing; the rest depends on your use case.

  • No separate cluster requirements for processing (integrated with Kafka).

  • Employs one-record-at-a-time processing to achieve millisecond processing latency, and supports event-time based windowing operations with the late arrival of records.

  • Supports Kafka Connect to connect to different applications and databases.

Read on for more details as well as a sample script to get started.

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