A system for processing streaming data in real time
Apache™ Storm adds reliable real-time data processing capabilities to Enterprise Hadoop. Storm on YARN is powerful for scenarios requiring real-time analytics, machine learning and continuous monitoring of operations.
Storm integrates with YARN via Apache Slider, YARN manages Storm while also considering cluster resources for data governance, security and operations components of a modern data architecture.
Storm is a distributed real-time computation system for processing large volumes of high-velocity data. Storm is extremely fast, with the ability to process over a million records per second per node on a cluster of modest size. Enterprises harness this speed and combine it with other data access applications in Hadoop to prevent undesirable events or to optimize positive outcomes.
Some of specific new business opportunities include: real-time customer service management, data monetization, operational dashboards, or cyber security analytics and threat detection.
Here are some typical “prevent” and “optimize” use cases for Storm.
|“Prevent” Use Cases||“Optimize” Use Cases|
Five characteristics make Storm ideal for real-time data processing workloads. Storm is:
A storm cluster has three sets of nodes:
Five key abstractions help to understand how Storm processes data:
Storm users define topologies for how to process the data when it comes streaming in from the spout. When the data comes in, it is processed and the results are passed into Hadoop.
Learn more about how the community is working to integrate Storm with Hadoop and improve its readiness for the enterprise.
Hortonworks is focused on developer productivity, enterprise readiness and operational simplicity of Storm.
For more info: Announcement Apache Storm 1.0
The Apache Storm open source community has already begun working on those themes.
|Apache Storm Version||Enhancements||HDP Versions||HDF Versions|
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