Kafka vs Kafka Streams: What are the differences?
Kafka: Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design; Kafka Streams: A client library for building applications and microservices. It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
Kafka and Kafka Streams are primarily classified as "Message Queue" and "Stream Processing" tools respectively.
Kafka is an open source tool with 13.1K GitHub stars and 6.99K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
According to the StackShare community, Kafka has a broader approval, being mentioned in 691 company stacks & 2401 developers stacks; compared to Kafka Streams, which is listed in 7 company stacks and 5 developer stacks.
What is Kafka?
What is Kafka Streams?
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Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.