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Apache Hive Cookbook

You're reading from Apache Hive Cookbook

Product type Paperback

Published in Apr 2016

Publisher Packt

ISBN-13 9781782161080

Length 268 pages

Edition 1st Edition

Languages

Java

Tools

Hadoop

Concepts

Data Processing

Authors (3):

Hanish Bansal

Saurabh Chauhan

Shrey Mehrotra

View More author details

Table of Contents (19) Chapters

Apache Hive Cookbook

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Preface

1. Developing Hive FREE CHAPTER

2. Services in Hive

3. Understanding the Hive Data Model

4. Hive Data Definition Language

5. Hive Data Manipulation Language

6. Hive Extensibility Features

7. Joins and Join Optimization

8. Statistics in Hive

9. Functions in Hive

10. Hive Tuning

11. Hive Security

12. Hive Integration with Other Frameworks

Index

Authorizing Hive

Hive authorization is about verifying that a user is authorized to perform a particular action. Authentication is about verifying the identity of a user, which is different from the authorization concept.

Hive can be used in the following different ways:

HCatalog API: Hive's HCatalog API is used to access Hive by many other frameworks, such as Apache Pig, MapReduce, Facebook Presto, Spark SQL, and Cloudera Impala. Using Hcatalog API, the users have direct access to HDFS data and Hive metadata. Hive metadata is directly accessible using the metastore server API.
Hive Command Line Interface (CLI): Using Hive CLI also, users have direct access to HDFS data and Hive metadata. Hive CLI directly interacts with the Hive metastore server. Currently, Hive CLI don't support rich authorization. In next versions of Hive, Hive CLI's implementation will be changed to provide better security, and also Hive CLI will interact with HiveSerer2 rather than directly interacting with the metastore...

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Authors (3)

Hanish Bansal

Hanish Bansal

Hanish Bansal is a software engineer with over 4 years of experience in developing big data applications. He loves to study emerging solutions and applications mainly related to big data processing, NoSQL, natural language processing, and neural networks. He has worked on various technologies such as Spring Framework, Hibernate, Hadoop, Hive, Flume, Kafka, Storm, and NoSQL databases, which include HBase, Cassandra, MongoDB, and search engines such as Elasticsearch. In 2012, he completed his graduation in Information Technology stream from Jaipur Engineering College and Research Center, Jaipur, India. He was also the technical reviewer of the book Apache Zookeeper Essentials. In his spare time, he loves to travel and listen to music. You can read his blog at http://hanishblogger.blogspot.in/ and follow him on Twitter at https://twitter.com/hanishbansal786.

See other products by Hanish Bansal

Saurabh Chauhan

Saurabh Chauhan

Saurabh Chauhan is a module lead with close to 8 years of experience in data warehousing and big data applications. He has worked on multiple Extract, Transform and Load tools, such as Oracle Data Integrator and Informatica as well as on big data technologies such as Hadoop, Hive, Pig, Sqoop, and Flume. He completed his bachelor of technology in 2007 from Vishveshwarya Institute of Engineering and Technology. In his spare time, he loves to travel and discover new places. He also has a keen interest in sports.

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Shrey Mehrotra

Shrey Mehrotra

Shrey Mehrotra has over 8 years of IT experience and, for the past 6 years, has been designing the architecture of cloud and big-data solutions for the finance, media, and governance sectors. Having worked on research and development with big-data labs and been part of Risk Technologies, he has gained insights into Hadoop, with a focus on Spark, HBase, and Hive. His technical strengths also include Elasticsearch, Kafka, Java, YARN, Sqoop, and Flume. He likes spending time performing research and development on different big-data technologies. He is the coauthor of the books Learning YARN and Hive Cookbook, a certified Hadoop developer, and he has also written various technical papers.

See other products by Shrey Mehrotra

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