Apache Storm vs Kafka Streams: What are the differences?

Apache Storm: Distributed and fault-tolerant realtime computation. Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate; 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.

Apache Storm and Kafka Streams can be categorized as "Stream Processing" tools.

Apache Storm is an open source tool with 5.81K GitHub stars and 3.94K GitHub forks. Here's a link to Apache Storm's open source repository on GitHub.

Spotify, Twitter, and Yelp are some of the popular companies that use Apache Storm, whereas Kafka Streams is used by Doodle, Bottega52, and Scout24. Apache Storm has a broader approval, being mentioned in 57 company stacks & 64 developers stacks; compared to Kafka Streams, which is listed in 7 company stacks and 8 developer stacks.