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

Apache Storm

187
277
+ 1
24
Samza

20
57
+ 1
0
Add tool

Apache Storm vs Samza: 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; Samza: A distributed stream processing framework. It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.

Apache Storm and Samza can be categorized as "Stream Processing" tools.

Some of the features offered by Apache Storm are:

  • Storm integrates with the queueing and database technologies you already use
  • Simple API
  • Scalable

On the other hand, Samza provides the following key features:

  • HIGH PERFORMANCE
  • HORIZONTALLY SCALABLE
  • EASY TO OPERATE

Apache Storm and Samza are both open source tools. It seems that Apache Storm with 5.82K GitHub stars and 3.95K forks on GitHub has more adoption than Samza with 569 GitHub stars and 244 GitHub forks.

Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Apache Storm
Pros of Samza
  • 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

    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 Samza?

    It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.

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

    What companies use Apache Storm?
    What companies use Samza?
    See which teams inside your own company are using Apache Storm or Samza.
    Sign up for StackShare EnterpriseLearn More

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

    What tools integrate with Apache Storm?
    What tools integrate with Samza?

    Blog Posts

    What are some alternatives to Apache Storm and Samza?
    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