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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
About the author
John Russell is a software developer and technical writer, and he's currently the documentation lead for the Cloudera Impala project. He has a broad range of database and SQL experience from previous roles on industry-leading teams. For DB2, he designed and coded the very first Information Center. For Oracle Database, he documented application development subjects and designed and coded the Project Tahiti doc search engine. For MySQL, he documented the InnoDB storage engine. Originally from Newfoundland, Canada, John now resides in Berkeley, California.
Summary
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.
