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Heron

20
54
+ 1
4
Pelican

63
84
+ 1
28
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Pros of Heron
Pros of Pelican
  • 1
    Highly Customizable
  • 1
    Support most popular container environment
  • 1
    Operation friendly
  • 1
    Realtime Stream Processing
  • 7
    Open source
  • 6
    Jinja2
  • 4
    Implemented in Python
  • 4
    Easy to deploy
  • 3
    Plugability
  • 2
    RestructuredText and Markdown support
  • 1
    Easy to customize
  • 1
    Can run on Github pages

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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.

What is Pelican?

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.

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What companies use Heron?
What companies use Pelican?
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What tools integrate with Heron?
What tools integrate with Pelican?
    No integrations found
    What are some alternatives to Heron and Pelican?
    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.
    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.
    Confluent
    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
    See all alternatives
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