Kafka Streams
Kafka Streams is a client library for building applications that continuously process data from and write data to Apache Kafka topics.
Kafka Streams in Action, Second Edition
The Kafka Streams API is the programming interface for building stream-processing applications with Kafka Streams.
- How it is used
- Builds client-side applications that transform Kafka event records and produce derived topic data in real time.
- How deep it goes
- Works through topology construction, required configuration, Serdes, common KStream operations, and Spring lifecycle integration.
- Examples
- Uses an uppercase-message topology, ZMart purchase masking and rewards routing, sales-trend processing, and loan-approval aggregation.
- What you can do after
- Build it from scratch by configuring a Java topology, transforming topic records, and starting a Kafka Streams application.
Read how Kafka Streams in Action, Second Edition explains Kafka Streams →
Kafka for Architects
The Kafka Streams API is the programming interface through which applications define Kafka Streams processing logic.
- How it is used
- Places low-latency transformation between Kafka input and output topics when downstream services need processed events.
- How deep it goes
- Explains DSL abstractions, custom processors, topologies, tasks, and failure reassignment, without a full implementation walkthrough.
- Examples
- Uses address routing, order filtering and mapping, fraud detection, and customer-data aggregation as processing scenarios.
- What you can do after
- Apply it via Kafka Streams to design a Java processing application and plan its topics, partitions, and deployment.
Apache Kafka in Action
Kafka Streams is a Java library for building applications that process Kafka records as streams.
- How it is used
- Processes Kafka records by consuming, filtering, transforming, or merging data and producing results.
- How deep it goes
- Explains partition-to-task assignment, instance failover, thread configuration, and monitoring; flags complex joins as an operational limit.
- Examples
- Illustrates a 12-partition filter spread across one or six instances and a core-system consolidation burdened by complex joins.
- What you can do after
- Operate/configure it by distributing partition-bound tasks across instances and threads, then monitoring failures and processing behavior.
Related concepts
- ksqlDBused by · ksqlDB is a streaming database system that uses SQL-like statements to process Apache Kafka event streams continuously.
- Apache Kafkauses · Apache Kafka is a distributed event-streaming platform for publishing, storing, processing, and consuming streams of records.
- Flinkalternative to · Apache Flink is an open-source distributed processing framework for stateful computation over bounded and unbounded data streams.
- KStreamuses · KStream is a Kafka Streams abstraction representing an unbounded stream of key-value records processed as independent events.
- taskhas part · A Kafka Streams task is an independently executable processing unit that processes assigned input partitions through a topology and its associated state stores.
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