Download full books in pdf Apache Spark in 24 Hours, Sams Teach Yourself by Jeffrey Aven

Apache Spark in 24 Hours, Sams Teach Yourself. Jeffrey Aven

Apache Spark in 24 Hours, Sams Teach Yourself


Apache-Spark-in-24-Hours.pdf
ISBN: 9780672338519 | 445 pages | 12 Mb
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  • Apache Spark in 24 Hours, Sams Teach Yourself
  • Jeffrey Aven
  • Page: 445
  • Format: pdf, ePub, fb2, mobi
  • ISBN: 9780672338519
  • Publisher: Sams
Download Apache Spark in 24 Hours, Sams Teach Yourself

Download full books in pdf Apache Spark in 24 Hours, Sams Teach Yourself by Jeffrey Aven

Overview

Apache Spark is rapidly becoming the preferred computing engine for Big Data systems. It's fast, fast, scalable, fault-tolerant, and exceptionally flexible and extensible. Now, in just 24 lessons of one hour or less, you can learn all the skills and techniques you'll need to successfully build practical Spark solutions. Each short, easy lesson builds on all that's come before: you'll learn all of Spark's essentials, and extend it to meet your unique challenges. Apache Spark in 24 Hours, Sams Teach Yourself covers all this, and much more: What Apache Spark does, and how it fits into the Big Data landscape How to deploy Spark local or in the cloud, and use the Spark Cluster Architecture How to program Spark applications with Scala, functional Python, and the Spark API How to use RDDs for caching, persistence, and output How to use Spark with both SQL and NoSQL How to work with advanced Spark programming techniques and common processing patterns How to extend spark with streaming, machine learning, R, and Sparkling Water How to manage Spark, extend it, and improve its performance Where Spark is headed, and how to prepare for the future Step-by-step instructions walk you through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test your knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help you avoid pitfalls. By the time you're finished, you'll be comfortable using Apache Spark to solve a wide spectrum of Big Data problems.

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