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Kafka Streams vs Faust: What are the differences?

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; Faust: A library for building streaming applications in Python. It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.

Kafka Streams and Faust can be primarily classified as "Stream Processing" tools.

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What is Faust?

It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.

What is Kafka Streams?

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.

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Jun 24 2020 at 4:42PM

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What are some alternatives to Faust and Kafka Streams?
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
Celery
Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
JUCE
It is a C++ framework for low-latency applications, with cross-platform GUI libraries to get your apps running on Mac OS X, Windows, Linux, iOS and Android.
See all alternatives