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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Distributed real-time stream processing engine for low-latency analytics
Apache Storm delivers scalable, fault-tolerant real-time computation across any language, enabling sub-second analytics and event-driven pipelines for enterprises with robust operational support.
Apache Storm is a distributed system that enables real‑time computation at scale. It targets developers and data engineers who need sub‑second latency for event‑driven applications, from fraud detection to live dashboards. The platform is language‑agnostic, allowing components to be written in Java, Python, Ruby, or any language that can speak its multilang protocol.
Storm provides primitives for defining topologies—directed graphs of spouts (data sources) and bolts (processing units). Its fault‑tolerance model automatically retries failed tuples and rebalances workloads across a cluster managed by Apache Zookeeper. Deployments can run on bare‑metal, virtual machines, or container orchestration platforms, with extensive documentation and community mailing lists to assist operations.
Backed by the Apache Software Foundation, Storm benefits from a mature codebase, active mailing lists for users, developers, and issue tracking on GitHub, and commercial backing from contributors such as YourKit. This ecosystem makes it a reliable choice for enterprises seeking a proven streaming engine.
When teams consider Apache Storm, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Real‑time fraud detection
Immediate identification and alerting of suspicious transactions
Clickstream analytics
Live dashboards that reflect user behavior as it happens
IoT sensor aggregation
Continuous processing of sensor streams for downstream analytics
Dynamic recommendation updates
On‑the‑fly model refreshes that improve personalization in real time
Storm supports any language via its multilang protocol; common choices include Java, Python, Ruby, and Clojure.
It tracks tuple acknowledgments, retries failed processing, and rebalances workloads automatically across the cluster.
A Storm cluster needs Apache Zookeeper for coordination, plus worker nodes that run the topology processes.
Storm processes each event as it arrives (true real‑time), whereas Spark Streaming works in micro‑batches.
Use the user@storm.apache.org mailing list for general questions, dev@storm.apache.org for development topics, and GitHub Issues for bugs and feature requests.
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