Kafka Streams is a lightweight library to build streaming applications. It supports basic streaming APIs such as join, filter, map and aggregate as well as local storage for common use cases such as windowing and sessions. Unlike other stream-processing platforms such as Apache Spark and Alpakka Kafka, Kafka Streams has been a good fit for scenarios that don't require large-scale distribution and parallel processing; hence we could get away without yet another piece of infrastructure such as cluster schedulers. Naturally, Kafka Streams has been a good choice when operating in the Kafka ecosystem. Kafka Streams is particularly useful when we have to process data strictly in order and exactly once. One particular use case of Kafka Streams is to build a change data capture (CDC) platform.
Kafka Streams is a lightweight library for building streaming applications. It's been designed with the goal of simplifying stream processing enough to make it easily accessible as a mainstream application programming model for asynchronous services. It can be a good alternative in scenarios where you want to apply a stream processing model to your problem, without embracing the complexity of running a cluster (usually introduced by full-fledged stream processing frameworks). New developments include ‘exactly once’ stream processing in a Kafka cluster. This was achieved by introducing idempotency in Kafka producers and allowing atomic writes across multiple partitions using the new Transactions API.
Kafka Streams is a lightweight library for building streaming applications. It's been designed with the goal of simplifying stream processing enough to make it easily accessible as a mainstream application programming model for asynchronous services. It can be a good alternative in scenarios where you want to apply a stream processing model to your problem without embracing the complexity of running a cluster (usually introduced by full-fledged stream processing frameworks).
