Getting Started with Impala

Interactive SQL for Apache Hadoop

Nonfiction, Computers, Database Management, Data Processing
Cover of the book Getting Started with Impala by John Russell, O'Reilly Media
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: John Russell ISBN: 9781491905722
Publisher: O'Reilly Media Publication: September 25, 2014
Imprint: O'Reilly Media Language: English
Author: John Russell
ISBN: 9781491905722
Publisher: O'Reilly Media
Publication: September 25, 2014
Imprint: O'Reilly Media
Language: English

Learn how to write, tune, and port SQL queries and other statements for a Big Data environment, using Impala—the massively parallel processing SQL query engine for Apache Hadoop. The best practices in this practical guide help you design database schemas that not only interoperate with other Hadoop components, and are convenient for administers to manage and monitor, but also accommodate future expansion in data size and evolution of software capabilities.

Written by John Russell, documentation lead for the Cloudera Impala project, this book gets you working with the most recent Impala releases quickly. Ideal for database developers and business analysts, the latest revision covers analytics functions, complex types, incremental statistics, subqueries, and submission to the Apache incubator.

Getting Started with Impala includes advice from Cloudera’s development team, as well as insights from its consulting engagements with customers.

  • Learn how Impala integrates with a wide range of Hadoop components
  • Attain high performance and scalability for huge data sets on production clusters
  • Explore common developer tasks, such as porting code to Impala and optimizing performance
  • Use tutorials for working with billion-row tables, date- and time-based values, and other techniques
  • Learn how to transition from rigid schemas to a flexible model that evolves as needs change
  • Take a deep dive into joins and the roles of statistics
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Learn how to write, tune, and port SQL queries and other statements for a Big Data environment, using Impala—the massively parallel processing SQL query engine for Apache Hadoop. The best practices in this practical guide help you design database schemas that not only interoperate with other Hadoop components, and are convenient for administers to manage and monitor, but also accommodate future expansion in data size and evolution of software capabilities.

Written by John Russell, documentation lead for the Cloudera Impala project, this book gets you working with the most recent Impala releases quickly. Ideal for database developers and business analysts, the latest revision covers analytics functions, complex types, incremental statistics, subqueries, and submission to the Apache incubator.

Getting Started with Impala includes advice from Cloudera’s development team, as well as insights from its consulting engagements with customers.

More books from O'Reilly Media

Cover of the book Head First C# by John Russell
Cover of the book Ansible: Up and Running by John Russell
Cover of the book Practical Tableau by John Russell
Cover of the book HTML5 Canvas by John Russell
Cover of the book PowerPoint 2007 for Starters: The Missing Manual by John Russell
Cover of the book Office 2011 for Macintosh: The Missing Manual by John Russell
Cover of the book Essential Silverlight 2 Up-to-Date by John Russell
Cover of the book Prototype Quick Reference by John Russell
Cover of the book Anonymizing Health Data by John Russell
Cover of the book High Performance Spark by John Russell
Cover of the book Understanding Context by John Russell
Cover of the book Perl in a Nutshell by John Russell
Cover of the book JRuby Cookbook by John Russell
Cover of the book Complete Web Monitoring by John Russell
Cover of the book Excel 2003: The Missing Manual by John Russell
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy