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Apache Hive Essentials

You're reading from Apache Hive Essentials Immerse yourself on a fantastic journey to discover the attributes of big data by using Hive

Product type Paperback

Published in Feb 2015

Publisher Packt

ISBN-13 9781783558575

Length 208 pages

Edition 1st Edition

Languages

SQL

Tools

Hive

Concepts

Data Processing

Author (1):

Dayong Du

View More author details

Table of Contents (12) Chapters

Preface

1. Overview of Big Data and Hive

2. Setting Up the Hive Environment FREE CHAPTER

3. Data Definition and Description

4. Data Selection and Scope

5. Data Manipulation

6. Data Aggregation and Sampling

7. Performance Considerations

8. Extensibility Considerations

9. Security Considerations

10. Working with Other Tools

Index

Hive Data Definition Language

Hive Data Definition Language (DDL) is a subset of Hive SQL statements that describe the data structure in Hive by creating, deleting, or altering schema objects such as databases, tables, views, partitions, and buckets. Most Hive DDL statements start with the keywords CREATE, DROP, or ALTER. The syntax of Hive DDL is very similar to the DDL in SQL. The comments in Hive start from --.

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Authors (1)

Dayong Du

Dayong Du

Dayong Du has all his career dedicated to enterprise data and analytics for more than 10 years, especially on enterprise use case with open source big data technology, such as Hadoop, Hive, HBase, Spark, etc. Dayong is a big data practitioner as well as author and coach. He has published the 1st and 2nd edition of Apache Hive Essential and coached lots of people who are interested to learn and use big data technology. In addition, he is a seasonal blogger, contributor, and advisor for big data start-ups, co-founder of Toronto big data professional association.

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