• Title O'Reilly® HBase: The Definitive Guide
• Author(s) Lars George
• Publisher: O'Reilly Media; 1 edition (August 22, 2011)
• Paperback: 432 pages
• eBook HTML and PDF (554 pages, 19.6 MB)
• Language: English
• ISBN-10: 1449396100
• ISBN-13: 978-1449396107
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Book Description

If your organization is looking for a storage solution to accommodate a virtually endless amount of data, this book will show you how Apache HBase can fulfill your needs. As the open source implementation of Google's BigTable architecture, HBase scales to billions of rows and millions of columns, while ensuring that write and read performance remain constant. HBase: The Definitive Guide provides the details you require, whether you simply want to evaluate this high-performance, non-relational database, or put it into practice right away.

HBase's adoption rate is beginning to climb, and several IT executives are asking pointed questions about this high-capacity database. This is the only book available to give you meaningful answers.

• Learn how to distribute large datasets across an inexpensive cluster of commodity servers
• Develop HBase clients in many programming languages, including Java, Python, and Ruby
• Get details on HBase's primary storage system, HDFS - Hadoop's distributed and replicated filesystem
• Learn how HBase's native interface to Hadoop's MapReduce framework enables easy development and execution of batch jobs that can scan entire tables
• Discover the integration between HBase and other facets of the Apache Hadoop project

• Lars George has been involved with HBase since 2007, and became a full HBase committer in 2009. He has spoken at various Hadoop User Group meetings, as well as large conferences such as FOSDEM in Brussels. He also started the Munich OpenHUG meetings. He now works closely with Cloudera to support Hadoop and HBase in and around Europe through technical support, consulting work, and training.

• Amazon

• Non-relational/NoSQL Databases
• Books by O'Reilly®
• Data Storage and Management
• Database Systems
• Data Analysis and Data Mining
• Database, Data Warehouse and Data Management

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