Apache Storm is a real-time computation system that makes it easy to process unbounded streams of data. It is a distributed, fault-tolerant, and scalable framework written predominantly in the Clojure programming language. Apache Storm allows for the processing of vast amounts of data with low latency, making it ideal for applications where real-time analytics and data processing are critical.
In the realm of Stream Processing, Apache Storm holds a significant position as a robust and efficient framework for handling streaming data. Stream Processing technologies focus on processing continuous streams of data in real-time, enabling organizations to derive insights and make decisions instantly based on the most up-to-date information available. Apache Storm, with its ability to handle high-speed data streams and ensure fault tolerance, is a key player in this category.
Apache Storm was founded in September 2011 by Nathan Marz and later open-sourced under the Apache Software Foundation in 2014. The motivation behind creating Apache Storm was to address the need for a distributed, real-time computation system that could handle the challenges of processing high-velocity data streams with low latency. Since its inception, Apache Storm has gained popularity for its near real-time processing capabilities and fault tolerance features.
In terms of current market share, Apache Storm continues to be widely used in industries requiring real-time data processing, such as financial services, telecommunications, and e-commerce. While it faces competition from other stream processing frameworks like Apache Flink and Spark Streaming, Apache Storm still maintains a strong presence in the market. With the increasing demand for real-time analytics and the growth of IoT devices generating continuous streams of data, Apache Storm is likely to see further adoption and potentially grow its market share in the future.
