Even as big data is turning the world upside down, the next phase of the revolution is already taking shape: real-time data analysis. This hands-on guide introduces you to Storm, a distributed, JVM-based system for processing streaming data. Through simple tutorials, sample Java code, and a complete real-world scenario, you’ll learn how to build fast, fault-tolerant solutions that process results as soon as the data arrives.
Discover how easy it is to set up Storm clusters for solving various problems, including continuous data computation, distributed remote procedure calls, and data stream processing.
- Learn how to program Storm components: spouts for data input and bolts for data transformation
- Discover how data is exchanged between spouts and bolts in a Storm topology
- Make spouts fault-tolerant with several commonly used design strategies
- Explore bolts—their life cycle, strategies for design, and ways to implement them
- Scale your solution by defining each component’s level of parallelism
- Study a real-time web analytics system built with Node.js, a Redis server, and a Storm topology
|Publisher:||O'Reilly Media, Incorporated|
|Product dimensions:||6.80(w) x 9.00(h) x 0.30(d)|
About the Author
Jonathan Leibiusky, Head of Research and Development at MercadoLibre, has been working in software development for more than 10 years. He has developed and contributed to several new and existing open source projects, including "Jedis", which is being used actively by VMWare and SpringSource.
Gabriel is a computer science student and works as an Software Architect in Mercadolibre (NASDAQ MELI) since 2007. He is tasked with researching technologies and developing projects. In the last year he has specialized in big data analysis, implementing Mercadolibre's hadoop cluster.
Dario has been working in software development for more than 10 years. Since 2004 he has specialized in large website, operations and performance. Today, Dario is the Chief Architect of MercadoLibre (NASDAQ MELI) where he leads the architecture team.