Includes bibliographical references and index.

Introduction -- Big data concepts -- Programming models for big data -- Tools for big data applications -- Comparing programming tools -- Choosing the right framework to tame big data.

"In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, are challenging current storage, processing, and analysis capabilities. New models, languages, systems, and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Programming Big Data Applications introduces and discusses models, programming frameworks and algorithms to process and analyze large amounts of data. In particular, the book provides an in-depth description of the properties and mechanisms of the main programming paradigms for Big Data analysis, including MapReduce, workflow, BSP, message passing, and SQL-like. Through programming examples, it also describes the most used frameworks for Big Data analysis like Hadoop, Spark, MPI, Hive, Storm, and others. We discuss and compare the different systems by highlighting the main features of each of them, their diffusion (both within their community of developers and users), and their main advantages and disadvantages in implementing Big Data analysis applications."-- Provided by publisher.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat reader.