It's a lot harder to make sense out of data when it's coming at full speed. Apache Storm's efficient stream processing capabilities are relied upon by giants like Twitter and Yahoo for swiftly extracting intelligence from their Big Data streams. Fault tolerant guarantees of Storm make it an invaluable and versatile platform in the Big Data landscape. It integrates seamlessly with battle-tested message queuing systems (like Kafka) and NoSQL databases (like Cassandra). Storm is built to run on the JVM but provides straightforward extensions for working with non-JVM languages like Ruby and Python. Storm Applied is a practical guide to using Apache Storm for the real-world tasks associated with processing and analyzing real-time data streams. The book starts by building a solid foundation of the Storm essentials. Then, it quickly dives into real-world case studies that will bring the novice up to speed with productionizing Storm: the knowledge needed to scale a high throughput stream processor and ensure smooth operation within a production cluster. It moves on to teach readers how to use Trident to treat streams as batches for solving a different class of problems, and covers the tools available within the Storm open source community that are crucial for any seasoned Storm developer. RETAIL SELLING POINTS Immediately useful practical guide Applies Storm to real-world use cases Takes Storm from development to a fully tuned and optimized production setup AUDIENCE While prior experience with Storm is not necessary, acquaintance with related Big Data problem solving is helpful. Basic understanding of Java or similar JVM language and concurrency is assumed. DESCRIBE THE TECHNOLOGY Storm is a tool that can be used for processing big data in real-time. Think performing real-time analysis of all the tweets going through Twitter.