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Realtime, distributed, fault-tolerant stream processing engine from Twitter
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What is Heron?

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.
Heron is a tool in the Stream Processing category of a tech stack.
Heron is an open source tool with GitHub stars and GitHub forks. Here’s a link to Heron's open source repository on GitHub

Who uses Heron?


20 developers on StackShare have stated that they use Heron.
Pros of Heron
Highly Customizable
Support most popular container environment
Operation friendly
Realtime Stream Processing

Heron Alternatives & Comparisons

What are some alternatives to Heron?
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.
Pelican is a static site generator that supports Markdown and reST syntax. Write your weblog entries directly with your editor of choice (vim!) in reStructuredText or Markdown.
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.
Apache NiFi
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.
Apache Storm
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.
See all alternatives

Heron's Followers
59 developers follow Heron to keep up with related blogs and decisions.