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Hadoop Application Architectures by Gwen Shapira, Jonathan Seidman, Ted Malaska, Mark Grover

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Foreword

Apache Hadoop has blossomed over the past decade.

It started in Nutch as a promising capability—the ability to scalably process petabytes. In 2005 it hadn’t been run on more than a few dozen machines, and had many rough edges. It was only used by a few folks for experiments. Yet a few saw promise there, that an affordable, scalable, general-purpose data storage and processing framework might have broad utility.

By 2007 scalability had been proven at Yahoo!. Hadoop now ran reliably on thousands of machines. It began to be used in production applications, first at Yahoo! and then at other Internet companies, like Facebook, LinkedIn, and Twitter. But while it enabled scalable processing of petabytes, the price of adoption was high, with no security and only a Java batch API.

Since then Hadoop’s become the kernel of a complex ecosystem. Its gained fine-grained security controls, high availability (HA), and a general-purpose scheduler (YARN).

A wide variety of tools have now been built around this kernel. Some, like HBase and Accumulo, provide online keystores that can back interactive applications. Others, like Flume, Sqoop, and Apache Kafka, help route data in and out of Hadoop’s storage. Improved processing APIs are available through Pig, Crunch, and Cascading. SQL queries can be processed with Apache Hive and Cloudera Impala. Apache Spark is a superstar, providing an improved and optimized batch API while also incorporating real-time stream processing, graph processing, and machine learning. Apache Oozie and Azkaban orchestrate and schedule many of the above.

Confused yet? This menagerie of tools can be overwhelming. Yet, to make effective use of this new platform, you need to understand how these tools all fit together and which can help you. The authors of this book have years of experience building Hadoop-based systems and can now share with you the wisdom they’ve gained.

In theory there are billions of ways to connect and configure these tools for your use. But in practice, successful patterns emerge. This book describes best practices, where each tool shines, and how best to use it for a particular task. It also presents common-use cases. At first users improvised, trying many combinations of tools, but this book describes the patterns that have proven successful again and again, sparing you much of the exploration.

These authors give you the fundamental knowledge you need to begin using this powerful new platform. Enjoy the book, and use it to help you build great Hadoop applications.

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