Strimzi provides a way to run an Apache Kafka cluster on Kubernetes in various deployment configurations.

Faust is a stream processing library, porting the ideas from Kafka Streams to Python.

It is used at Robinhood to build high performance distributed systems and real-time data pipelines that process billions of events every day.

Faust provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink,

It does not use a DSL, it’s just Python! This means you can use all your favorite Python libraries when stream processing: NumPy, PyTorch, Pandas, NLTK, Django, Flask, SQLAlchemy, ++

Faust requires Python 3.6 or later for the new async/await syntax, and variable type annotations.

Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.
Fast
A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients.
Scalable
Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers
Durable
Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact.
Distributed by Design
Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
Apache Feather