Programming Hive : Data Warehouse and Query Language for Hadoop

Edward Capriolo

ISBN 10: 1449319335 / ISBN 13: 9781449319335

Published by O'reilly Media Okt 2012, 2012

About this Item

Neuware - Need to move a relational database application to Hadoop This comprehensive guide introduces you to Apache Hive, Hadoop's data warehouse infrastructure. You'll quickly learn how to use Hive's SQL dialect - HiveQL - to summarize, query, and analyze large datasets stored in Hadoop's distributed filesystem.This example-driven guide shows you how to set up and configure Hive in your environment, provides a detailed overview of Hadoop and MapReduce, and demonstrates how Hive works within the Hadoop ecosystem. You'll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data. Use Hive to create, alter, and drop databases, tables, views, functions, and indexes Customize data formats and storage options, from files to external databases Load and extract data from tables - and use queries, grouping, filtering, joining, and other conventional query methods Gain best practices for creating user defined functions (UDFs) Learn Hive patterns you should use and anti-patterns you should avoid Integrate Hive with other data processing programs Use storage handlers for NoSQL databases and other datastores Learn the pros and cons of running Hive on Amazon's Elastic MapReduce.

Seller Inventory # 9781449319335

Bibliographic Details

• Title: Programming Hive : Data Warehouse and Query Language for Hadoop
• Publisher: O'reilly Media Okt 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 1449319335
• ISBN 13: 9781449319335
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoop’s data warehouse infrastructure. You’ll quickly learn how to use Hive’s SQL dialect―HiveQL―to summarize, query, and analyze large datasets stored in Hadoop’s distributed filesystem.

This example-driven guide shows you how to set up and configure Hive in your environment, provides a detailed overview of Hadoop and MapReduce, and demonstrates how Hive works within the Hadoop ecosystem. You’ll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data.

• Use Hive to create, alter, and drop databases, tables, views, functions, and indexes
• Customize data formats and storage options, from files to external databases
• Load and extract data from tables―and use queries, grouping, filtering, joining, and other conventional query methods
• Gain best practices for creating user defined functions (UDFs)
• Learn Hive patterns you should use and anti-patterns you should avoid
• Integrate Hive with other data processing programs
• Use storage handlers for NoSQL databases and other datastores
• Learn the pros and cons of running Hive on Amazon’s Elastic MapReduce

About the Authors

Edward Capriolo is currently System Administrator at Media6degrees where he helps design and maintain distributed data storage systems for the internet advertising industry.

Edward is a member of the Apache Software Foundation and a committer for the Hadoop-Hive project. He has experience as a developer as well Linux and network administrator and enjoys the rich world of open source software.

Jason Rutherglen is a software architect at Think Big Analytics and specializes in Big Data, Hadoop, search, and security.

Dean Wampler is a Principal Consultant at Think Big Analytics, where he specializes in "Big Data" problems and tools like Hadoop and Machine Learning. Besides Big Data, he specializes in Scala, the JVM ecosystem, JavaScript, Ruby, functional and object-oriented programming, and Agile methods. Dean is a frequent speaker at industry and academic conferences on these topics. He has a Ph.D. in Physics from the University of Washington.