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.
Apache Storm is a tool in the Stream Processing category of a tech stack.
Apache Storm is an open source tool with 6.2K GitHub stars and 4.1K GitHub forks. Here’s a link to Apache Storm's open source repository on GitHub
Who uses Apache Storm?
54 companies reportedly use Apache Storm in their tech stacks, including Spotify, Twitter, and trivago.
111 developers on StackShare have stated that they use Apache Storm.
Pros of Apache Storm
May 29 2015 at 9:25AM
Apache Storm's Features
- Storm integrates with the queueing and database technologies you already use
- Simple API
- Fault tolerant
- Guarantees data processing
- Use with any language
- Easy to deploy and operate
- Free and open source
Apache Storm Alternatives & Comparisons
What are some alternatives to Apache Storm?
See all alternatives
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 is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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.
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 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.