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Hadoop For Dummies

Hadoop For Dummies

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About the Book

Big data has become an industry in itself, and Hadoop is the vehicle that extracts information from the growing massive data sets today's companies are holding. Big-data processing is a cross section of technical disciplines, including distributed systems (processing, storage, and networking), systems deployment and management, data science, and software development. Hadoop For Dummies educates readers about the new value of big data and how Hadoop can exploit that data to create real value:
Part 1 explains the challenge that led to the development of Hadoop and defines the MapReduce paradigm in easy-to-understand language, introducting some of the patterns that work (and some that don't) within this distributed data processing framework. Readers work through a set of simple examples in Hadoop, and learn the MapReduce programming model.
Part 2 steps back and explores the economic benefits of Hadoop and its general ROI. The book also explores the Hadoop ecosystem and some ways that readers can easily and quickly get their Hadoop cluster up and running with private and public (cloud) options. Finally, for a private cluster, readers take a look at the construction of a Hadoop cluster based upon a given usage model, and analyze how to size your cluster for your application and also how to scale it as your data and processing needs grow.
Part 3 introduces the most important aspect of Hadoop, its applications. In this part, the book details some of the most valuable applications of Hadoop from data mining, web analytics and personalization, large scale text processing, and the fastest growing sector; data science. Readers learn not only how companies use Hadoop across a spectrum of problems, but also ways to view a problem in order to solve it in Hadoop.
Part 4 explores the ecosystem that is rapidly growing around Hadoop, from simplifying Hadoop queries with Pig, to the powerful layers that are being built on Hadoop including data warehouses with Hive, and non-relational data processing with HBase. The book also introduces some of the other big data processing frameworks that provide unique capabilities (Storm for real-time stream processing, and Spark for in-memory cluster computing).
Part 5 provides the part of tens, where readers learn ways to optimize and improve the value of your Hadoop cluster. Readers explore ways to maximize their Hadoop investment, patterns for MapReduce programming, and also pitfalls to avoid in building a Hadoop cluster.

Series features: The book presents the topic consistently with the For Dummies style -- concise, lighthearted, and conversational.

About the Author

Roman B. Melnyk , Ph.D., is a senior member of the DB2 Information Development team. Paul Zikopoulos is the executive for Technical Sales in IBM Information Management, and an award winning speaker and author of 16 books. Dirk deRoos is the world-wide technical sales lead for IBM's Hadoop offering: InfoSphere BigInsights. Bruce Brown is a technical specialist focused on structured data in Hadoop on IBM's World-Wide Big Data team. Rafael Coss is the leader of the IBM's Big Data Enablement team.



Table of Contents:
Introduction Part I: Getting Started with Hadoop Chapter 1: Introducing Hadoop and Seeing What It's Good For Chapter 2: Common Use Cases for Big Data in Hadoop Chapter 3: Setting Up Your Hadoop Environment Part II: How Hadoop Works Chapter 4: Storing Data in Hadoop: The Hadoop Distributed File System Chapter 5: Reading and Writing Data Chapter 6: MapReduce Programming Chapter 7: Frameworks for Processing Data in Hadoop: YARN and MapReduce Chapter 8: Pig: Hadoop Programming Made Easier Chapter 9: Statistical Analysis in Hadoop Chapter 10: Developing and Scheduling Application Workflows with Oozie Part III: Hadoop and Structured Data Chapter 11: Hadoop and the Data Warehouse: Friends or Foes? Chapter 12: Extremely Big Tables: Storing Data in HBase Chapter 13: Applying Structure to Hadoop Data with Hive Chapter 14: Integrating Hadoop with Relational Databases Using Sqoop Chapter 15: The Holy Grail: Native SQL Access to Hadoop Data Part IV: Administering and Configuring Hadoop Chapter 16: Deploying Hadoop Chapter 17: Administering Your Hadoop Cluster Part V: The Part of Tens Chapter 18: Ten Hadoop Resources Worthy of a Bookmark Chapter 19: Ten Reasons to Adopt Hadoop Index


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Product Details
  • ISBN-13: 9788126550517
  • Publisher: Wiley India Pvt Ltd
  • Binding: Paperback
  • No of Pages: 412
  • ISBN-10: 8126550511
  • Publisher Date: 2014
  • Language: English

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