Real-time data processing in no longer a luxury exercised by a few big companies but has become a necessity for businesses that want to compete, and Apache Storm is one of the de facto standards for developing real-time processing pipelines. The key features of Storm are that it is horizontally scalable, is fault tolerant, and provides guaranteed message processing. Storm can solve various types of analytic problem: machine learning, log processing, graph analysis, and so on.
Mastering Storm will serve both as a getting started guide to inexperienced developers and as a reference for implementing advanced use cases with Storm for experienced developers. In the first two chapters, you will learn the basics of a Storm topology and various components of a Storm cluster. In the later chapters, you will learn how to build a Storm application that can interact with various other big data technologies and how to create transactional topologies. Finally, the last two chapters cover case studies for log processing and machine learning. We are also going to cover how we can use the Storm scheduler to assign delicate work to delicate machines.
