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Storm Applied is a practical guide to using Apache Storm for the real-world tasks associated with processing and analyzing real-time data streams. This immediately useful book starts by building a solid foundation of Storm essentials so that you learn how to think about designing Storm solutions the right way from day one. But it quickly dives into real-world case studies that will bring the novice up to speed with productionizing Storm.
It's hard to make sense out of data when it's coming at you fast. Like Hadoop, Storm processes large amounts of data but it does it reliably and in real time, guaranteeing that every message will be processed. Storm allows you to scale with your data as it grows, making it an excellent platform to solve your big data problems.
Storm Applied is an example-driven guide to processing and analyzing real-time data streams. This immediately useful book starts by teaching you how to design Storm solutions the right way. Then, it quickly dives into real-world case studies that show you how to scale a high-throughput stream processor, ensure smooth operation within a production cluster, and more. Along the way, you'll learn to use Trident for stateful stream processing, along with other tools from the Storm ecosystem.
This book moves through the basics quickly. While prior experience with Storm is not assumed, some experience with big data and real-time systems is helpful.
Sean Allen, Matthew Jankowski, and Peter Pathirana lead the development team for a high-volume, search-intensive commercial web application at TheLadders.
Will no doubt become the definitive practitioner’s guide for Storm users.
The book’s practical approach to Storm will save you a lot of hassle and a lot of time.
Great introduction to distributed computing with lots of real-world examples.
Go beyond the MapReduce way of thinking to solve big data problems.
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