The most common use case for Hive is to query data in Hadoop. To achieve this, we need to write and execute a SELECT statement. The typical work done by the SELECT statement is to project the whole row (with SELECT *) or specified columns (with SELECT column1, column2, ...) from a table, with or without conditions.Most simple SELECT statements will not trigger a Yarn job. Instead, a dump task is created just for dumping the data, such as the hdfs dfs -cat command. The SELECT statement is quite often used with the FROM and DISTINCT keywords. A FROM keyword followed by a table is where SELECT projects data. The DISTINCT keyword used after SELECT ensures only unique rows or combination of columns are returned from the table. In addition, SELECT also supports columns combined with user-defined functions, IF(), or a CASE WHEN THEN ELSE END statement, and regular...
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Book Overview & Buying
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Table Of Contents
Apache Hive Essentials - Second Edition
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Apache Hive Essentials
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Overview of this book
In this book, we prepare you for your journey into big data by frstly introducing you to backgrounds in the big data domain, alongwith the process of setting up and getting familiar with your Hive working environment.
Next, the book guides you through discovering and transforming the values of big data with the help of examples. It also hones your skills in using the Hive language in an effcient manner. Toward the end, the book focuses on advanced topics, such as performance, security, and extensions in Hive, which will guide you on exciting adventures on this worthwhile big data journey.
By the end of the book, you will be familiar with Hive and able to work effeciently to find solutions to big data problems
Table of Contents (12 chapters)
Preface
Free Chapter
Overview of Big Data and Hive
Setting Up the Hive Environment
Data Definition and Description
Data Correlation and Scope
Data Manipulation
Data Aggregation and Sampling
Performance Considerations
Extensibility Considerations
Security Considerations
Working with Other Tools
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