Summary
Hadoop in Practice collects 85 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like querying big data using Pig or writing a log file loader. You'll explore each problem step by step, learning both how to build and deploy that specific solution along with the thinking that went into its design. As you work through the tasks, you'll find yourself growing more comfortable with Hadoop and at home in the world of big data.
About the TechnologyHadoop is an open source MapReduce platform designed to query and analyze data distributed across large clusters. Especially effective for big data systems, Hadoop powers mission-critical software at Apple, eBay, LinkedIn, Yahoo, and Facebook. It offers developers handy ways to store, manage, and analyze data.
About the BookHadoop in Practice collects 85 battle-tested examples and presents them in a problem/solution format. It balances conceptual foundations with practical recipes for key problem areas like data ingress and egress, serialization, and LZO compression. You'll explore each technique step by step, learning how to build a specific solution along with the thinking that went into it. As a bonus, the book's examples create a well-structured and understandable codebase you can tweak to meet your own needs.
This book assumes the reader knows the basics of Hadoop.
 Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. 
What's Inside<ul>
<li>Conceptual overview of Hadoop and MapReduce</li>
<li>85 practical, tested techniques</li>
<li>Real problems, real solutions</li>
<li>How to integrate MapReduce and R</li>
</ul>
Table of Contents<ol>PART 1 BACKGROUND AND FUNDAMENTALS<li>Hadoop in a heartbeat</li>PART 2 DATA LOGISTICS<li>Moving data in and out of Hadoop</li>
<li>Data serialization?working with text and beyond</li>PART 3 BIG DATA PATTERNS<li>Applying MapReduce patterns to big data</li>
<li>Streamlining HDFS for big data</li>
<li>Diagnosing and tuning performance problems</li>PART 4 DATA SCIENCE<li>Utilizing data structures and algorithms</li>
<li>Integrating R and Hadoop for statistics and more</li>
<li>Predictive analytics with Mahout</li>PART 5 TAMING THE ELEPHANT<li>Hacking with Hive</li>
<li>Programming pipelines with Pig</li>
<li>Crunch and other technologies</li>
<li>Testing and debugging</li>
</ol>

Hadoop in Practice: Includes 85 Techniques

Computers & Technology

Holmes, Alex

Computer Science

Manning

Summary Hadoop in Practice, Second Edition provides over 100 tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN and integrating Kafka, Impala, and Spark SQL with Hadoop. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book It's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. This completely revised edition covers changes and new features in Hadoop core, including MapReduce 2 and YARN. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, this is the most practical, up-to-date coverage of Hadoop available. Readers need to know a programming language like Java and have basic familiarity with Hadoop. What's Inside <ul>
<li>Thoroughly updated for Hadoop 2</li>
<li>How to write YARN applications</li>
<li>Integrate real-time technologies like Storm, Impala, and Spark</li>
<li>Predictive analytics using Mahout and RR </li>
<li>Readers need to know a programming language like Java and have basic familiarity with Hadoop. </li>
</ul>
 About the Author Alex Holmes works on tough big-data problems. He is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects. Table of Contents <ol>PART 1 BACKGROUND AND FUNDAMENTALS<li>Hadoop in a heartbeat</li>
<li>Introduction to YARN</li>PART 2 DATA LOGISTICS<li>Data serialization—working with text and beyond</li>
<li>Organizing and optimizing data in HDFS</li>
<li>Moving data into and out of Hadoop</li>PART 3 BIG DATA PATTERNS<li>Applying MapReduce patterns to big data</li>
<li>Utilizing data structures and algorithms at scale</li>
<li>Tuning, debugging, and testing</li>PART 4 BEYOND MAPREDUCE<li>SQL on Hadoop</li>
<li> Writing a YARN application</li>
</ol>

Hadoop in Practice: Includes 104 Techniques

Foreword by Charlotte Fox Weber, psychotherapist, writer and co-founder of Examined Life. We live in a super-connected world, yet men specifically, struggle to connect and share. This is changing... but not quickly enough. Award-winning podcaster Alex Holmes sets out to accelerate this shift, debunking lingering myths around masculinity, love and connection by exploring what causes this sense of loneliness. Starting with 'Real Man Myths' and features designed to encourage us to open up and share, Alex motivates us to move from: * Ignoring to Acknowledging. * Being Closed to Opening Up. * Can't to Can. * Avoiding to Embracing. * Expecting to Accepting. Sharing his experiences on his podcast and as a young British black man, Time to Talk is a love letter to all the men who have lost their way and to the women that love them.

Time to Talk: How Men Think About Love, Belonging and Connection

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