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Abstract
Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoops data warehouse infrastructure. Youll quickly learn how to use Hives SQL dialectHiveQLto summarize, query, and analyze large datasets stored in Hadoops 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. Youll 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 tablesand 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 Amazons Elastic MapReduce
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Book Description:Dive into the world of SQL on Hadoop and get the most out of your Hive data warehouses. This book is your go-to resource for using Hive: authors Scott Shaw, Ankur Gupta, David Kjerrumgaard, and Andreas Francois Vermeulen take you through learning HiveQL, the SQL-like language specific to Hive, to analyze, export, and massage the data stored across your Hadoop environment. From deploying Hive on your hardware or virtual machine and setting up its initial configuration to learning how Hive interacts with Hadoop, MapReduce, Tez and other big data technologies,Practical Hive gives you a detailed treatment of the software.In addition, this book discusses the value of open source software, Hive performance tuning, and how to leverage semi-structured and unstructured data.
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March 2023
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