This book takes you on a fantastic journey to discover the attributes of big data using Apache Hive.

Key Features

• Grasp the skills needed to write efficient Hive queries to analyze the Big Data
• Discover how Hive can coexist and work with other tools within the Hadoop ecosystem
• Uses practical, example-oriented scenarios to cover all the newly released features of Apache Hive 2.3.3

Book Description

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

What you will learn

• Create and set up the Hive environment
• Discover how to use Hive's definition language to describe data
• Discover interesting data by joining and filtering datasets in Hive
• Transform data by using Hive sorting, ordering, and functions
• Aggregate and sample data in different ways
• Boost Hive query performance and enhance data security in Hive
• Customize Hive to your needs by using user-defined functions and integrate it with other tools

Who This Book Is For

If you are a data analyst, developer, or simply someone who wants to quickly get started with Hive to explore and analyze Big Data in Hadoop, this is the book for you. Since Hive is an SQL-like language, some previous experience with SQL will be useful to get the most out of this book.

Table of Contents

1. OVERVIEW OF BIG DATA AND HIVE
2. SETTING UP THE HIVE ENVIRONMENT
3. DATA DEFINITION AND DESCRIPTION
4. Data Correlation and Scope
5. DATA MANIPULATION
6. DATA AGGREGATION AND SAMPLING
7. Extensibility Considerations
8. Working with Other Tools
9. Performance Considerations
10. Security Considerations