Programming Hive Data Warehouse and Query Language for Hadoop

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