Need advice about which tool to choose?Ask the StackShare community!

Apache Storm

177
252
+ 1
24
Kafka Streams

290
363
+ 1
0
Add tool

Apache Storm vs Kafka Streams: What are the differences?

Apache Storm: Distributed and fault-tolerant realtime computation. 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; Kafka Streams: A client library for building applications and microservices. 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 Storm and Kafka Streams can be categorized as "Stream Processing" tools.

Apache Storm is an open source tool with 5.81K GitHub stars and 3.94K GitHub forks. Here's a link to Apache Storm's open source repository on GitHub.

Spotify, Twitter, and Yelp are some of the popular companies that use Apache Storm, whereas Kafka Streams is used by Doodle, Bottega52, and Scout24. Apache Storm has a broader approval, being mentioned in 57 company stacks & 64 developers stacks; compared to Kafka Streams, which is listed in 7 company stacks and 8 developer stacks.

Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of Apache Storm
Pros of Kafka Streams
  • 10
    Flexible
  • 6
    Easy setup
  • 3
    Clojure
  • 3
    Event Processing
  • 2
    Real Time
    Be the first to leave a pro

    Sign up to add or upvote prosMake informed product decisions

    Sign up to add or upvote consMake informed product decisions

    No Stats
    - No public GitHub repository available -

    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 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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Apache Storm?
    What companies use Kafka Streams?
    See which teams inside your own company are using Apache Storm or Kafka Streams.
    Sign up for Private StackShareLearn More

    Sign up to get full access to all the companiesMake informed product decisions

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

    Blog Posts

    Jun 24 2020 at 4:42PM

    Pinterest

    Amazon S3KafkaHBase+4
    4
    1065
    What are some alternatives to Apache Storm and Kafka Streams?
    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.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    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.
    See all alternatives