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Apache Storm

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Pros of Apache Storm
Pros of Kafka
  • 8
  • 5
    Easy setup
  • 3
  • 3
    Event Processing
  • 2
    Real Time
  • 116
  • 110
  • 83
  • 77
  • 62
  • 34
  • 17
  • 13
    Open source
  • 9
    Written in Scala and java. Runs on JVM
  • 6
    Message broker + Streaming system
  • 4
    Avro schema integration
  • 2
    Suport Multiple clients
  • 2
  • 2
    Partioned, replayable log
  • 1
  • 1
    Extremely good parallelism constructs
  • 1
    Simple publisher / multi-subscriber model
  • 1

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Cons of Apache Storm
Cons of Kafka
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    • 25
      Needs Zookeeper
    • 24
      Non-Java clients are second-class citizens
    • 6
      Operational difficulties
    • 1
      Terrible Packaging

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    What is 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.

    What is Kafka?

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

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    What companies use Apache Storm?
    What companies use Kafka?

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    What tools integrate with Apache Storm?
    What tools integrate with Kafka?

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    Blog Posts

    Jun 24 2020 at 4:42PM logo


    Amazon S3KafkaHBase+4
    MySQLKafkaApache Spark+6
    Jan 7 2020 at 5:09PM logo

    Ably Realtime

    Jun 6 2019 at 5:11PM logo


    What are some alternatives to Apache Storm and Kafka?
    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.
    Amazon Kinesis
    Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
    Apache Flume
    It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
    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.
    See all alternatives
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