Alternatives to Heron logo

Alternatives to Heron

Apache Flink, Pelican, Kafka Streams, Apache NiFi, and Apache Storm are the most popular alternatives and competitors to Heron.
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What is Heron and what are its top alternatives?

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 3.6K GitHub stars and 621 GitHub forks. Here’s a link to Heron's open source repository on GitHub

Top Alternatives to Heron

  • Apache Flink

    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

    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. ...

  • Kafka Streams

    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

    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

    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

    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 ...

  • KSQL

    KSQL

    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. ...

  • Kapacitor

    Kapacitor

    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. ...

Heron alternatives & related posts

Apache Flink logo

Apache Flink

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Fast and reliable large-scale data processing engine
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PROS OF APACHE FLINK
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    Unified batch and stream processing
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    Easy to use streaming apis
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    Out-of-the box connector to kinesis,s3,hdfs
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    Open Source
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    Low latency
CONS OF APACHE FLINK
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    Surabhi Bhawsar
    Technical Architect at Pepcus · | 7 upvotes · 548.2K views
    Shared insights
    on
    KafkaKafkaApache FlinkApache Flink

    I need to build the Alert & Notification framework with the use of a scheduled program. We will analyze the events from the database table and filter events that are falling under a day timespan and send these event messages over email. Currently, we are using Kafka Pub/Sub for messaging. The customer wants us to move on Apache Flink, I am trying to understand how Apache Flink could be fit better for us.

    See more

    I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?

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    Pelican logo

    Pelican

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    A static site generator, written in Python, that requires no database or server-side logic
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    PROS OF PELICAN
    • 7
      Open source
    • 6
      Jinja2
    • 4
      Implemented in Python
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      Easy to deploy
    • 3
      Plugability
    • 2
      RestructuredText and Markdown support
    • 1
      Easy to customize
    • 1
      Can run on Github pages
    CONS OF PELICAN
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      related Pelican posts

      Kafka Streams logo

      Kafka Streams

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      A client library for building applications and microservices
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      PROS OF KAFKA STREAMS
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        CONS OF KAFKA STREAMS
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          Apache NiFi logo

          Apache NiFi

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          A reliable system to process and distribute data
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          PROS OF APACHE NIFI
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            Visual Data Flows using Directed Acyclic Graphs (DAGs)
          • 8
            Free (Open Source)
          • 7
            Simple-to-use
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            Reactive with back-pressure
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            Scalable horizontally as well as vertically
          • 4
            Fast prototyping
          • 3
            Bi-directional channels
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            Data provenance
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            Built-in graphical user interface
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            End-to-end security between all nodes
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            Can handle messages up to gigabytes in size
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            Hbase support
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            Kudu support
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            Hive support
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            Slack integration
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            Support for custom Processor in Java
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            Lot of articles
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            Lots of documentation
          CONS OF APACHE NIFI
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            HA support is not full fledge
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            Memory-intensive

          related Apache NiFi posts

          I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

          For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

          See more
          Apache Storm logo

          Apache Storm

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          Distributed and fault-tolerant realtime computation
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          PROS OF APACHE STORM
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            Flexible
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            Easy setup
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            Clojure
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            Event Processing
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            Real Time
          CONS OF APACHE STORM
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            Marc Bollinger
            Infra & Data Eng Manager at Thumbtack · | 5 upvotes · 466.8K views

            Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.

            We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.

            We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.

            To find out more, read our 2017 engineering blog post about the migration!

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            Confluent logo

            Confluent

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            A stream data platform to help companies harness their high volume real-time data streams
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            PROS OF CONFLUENT
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              No hypercloud lock-in
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              Dashboard for kafka insight
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              Zero devops
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              Free for casual use
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              Easily scalable
            CONS OF CONFLUENT
            • 1
              Proprietary

            related Confluent posts

            KSQL logo

            KSQL

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            Open source streaming SQL for Apache Kafka
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            PROS OF KSQL
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              Streamprocessing on Kafka
            • 2
              SQL syntax with windowing functions over streams
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              Easy transistion for SQL Devs
            CONS OF KSQL
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              Kapacitor logo

              Kapacitor

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              A real-time streaming data processing engine
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              PROS OF KAPACITOR
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                CONS OF KAPACITOR
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