Instant Apache Hive Essentials How-to
By Darren Lee
Publisher: Packt Publishing
Released: June 2013
Pages: 76

In Detail

Hadoop provides a robust framework for building distributed applications, but working directly with Hadoop requires writing a lot of code. Adding structure to data and using a higher-level language such as SQL makes working with Hadoop both easier and faster.

"Instant Apache Hive Essentials How-to" contains a series of practical recipes that introduce the power and flexibility of Hive. Starting with your first query, this book will provide step-by-step instructions and behind-the-scenes explanations for how to effectively write MapReduce jobs with SQL.

This book looks at how Hive transforms SQL statements into MapReduce jobs and demonstrates how you can extend Hive to support your own use cases. Its recipes will teach you how to leverage the scale of Hadoop while retaining the benefits of using a structured query language.You will learn how Hive translates a query into MapReduce jobs and explore how to structure your queries for better performance. You will extend Hive to understand your own file formats, simplifying the loading of data into the warehouse. You will finally add your own custom functions to Hive to support whatever use cases you may have.

"Instant Apache Hive Essentials How-to" is a quick introduction for adding Hive to your data toolkit. It is packed with high-level instructions for making Hive work as well as drawing connections to the underlying Hadoop framework to explain how things happen.

Approach

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks.This book provides quick recipes for using Hive to read data in various formats, efficiently querying this data, and extending Hive with any custom functions you may need to insert your own logic into the data pipeline.

Who this book is for

This book is written for data analysts and developers who want to use their current knowledge of SQL to be more productive with Hadoop. It assumes that readers are comfortable writing SQL queries and are familiar with Hadoop at the level of the classic WordCount example.

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