Amazon WorkSpaces Streaming Protocol

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.

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

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.

KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.

It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform. It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.

It is a streaming platform for mission critical workloads. Kafka® compatible, No Zookeeper®, no JVM, and no code changes required. Use all your favorite open source tooling - 10x faster.

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.

It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.

Heron is realtime analytics platform developed by Twitter. It is the direct successor of Apache Storm, built to be backwards compatible with Storm's topology API but with a wide array of architectural improvements.

It is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads.

It is a distributed stream processing engine written in Rust, designed to efficiently perform stateful computations on streams of data.

It is a free and open source server and web application, written in Node.js, that allows adding human intelligence to data streaming in scenarios where computers are not suitable to make educated enough choices. In just a couple lines of code it will ingest your data stream, open an HTTP server with a WebApplication that will be fed with all the data from the stream. Now you and your team can add decisions to each item of your data stream.

It is an open source framework that lets you build a data pipeline by simply attaching a decorator to a Python function. All you need to do is describe where your input is coming from and where your output should be written, and BuildFlow handles the rest.

riko is a pure Python library for analyzing and processing streams of structured data. riko has synchronous and asynchronous APIs, supports parallel execution, and is well suited for processing RSS feeds. riko also supplies a command-line interface for executing flows, i.e., stream processors aka workflows.