By Philip Kromer,Russell Jurney

Finding styles in colossal occasion streams may be tricky, yet studying how to define them doesn’t need to be. This special hands-on advisor indicates you the way to unravel this and plenty of different difficulties in large-scale info processing with basic, enjoyable, and chic instruments that leverage Apache Hadoop. You’ll achieve a realistic, actionable view of massive info via operating with actual information and actual problems.

Perfect for rookies, this book’s strategy also will entice skilled practitioners who are looking to brush up on their abilities. half I explains how Hadoop and MapReduce paintings, whereas half II covers many analytic styles you should use to technique any facts. As you're employed via a number of routines, you’ll additionally methods to use Apache Pig to technique data.

  • Learn the required mechanics of operating with Hadoop, together with how facts and computation circulate round the cluster
  • Dive into map/reduce mechanics and construct your first map/reduce activity in Python
  • Understand tips to run chains of map/reduce jobs within the type of Pig scripts
  • Use a real-world dataset—baseball functionality statistics—throughout the book
  • Work with examples of numerous analytic styles, and research while and the place chances are you'll use them

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Information uncertainty generally exists in lots of purposes, and an doubtful info circulate is a chain of doubtful tuples that arrive swiftly. in spite of the fact that, conventional thoughts for deterministic information streams can't be utilized to accommodate info uncertainty without delay as a result exponential development of attainable answer house.

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