Book Image

Apache Hive Essentials. - Second Edition

By : Dayong Du
Book Image

Apache Hive Essentials. - Second Edition

By: Dayong Du

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

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Overview of Big Data and Hive
Overview of Big Data and Hive
A short history
Introducing big data
The relational and NoSQL databases versus Hadoop
Batch, real-time, and stream processing
Overview of the Hadoop ecosystem
Hive overview
Summary
2
Setting Up the Hive Environment
Setting Up the Hive Environment
Installing Hive from Apache
Installing Hive from vendors
Using Hive in the cloud
Using the Hive command
Using the Hive IDE
Summary
3
Data Definition and Description
Data Definition and Description
Understanding data types
Data type conversions
Data Definition Language
Database
Tables
Partitions
Buckets
Views
Summary
4
Data Correlation and Scope
Data Correlation and Scope
Project data with SELECT
Filtering data with conditions
Linking data with JOIN
Combining data with UNION
Summary
5
Data Manipulation
Data Manipulation
Data exchanging with LOAD
Data exchange with INSERT
Data exchange with [EX|IM]PORT
Data sorting
Functions
Transactions and locks
Summary
6
Data Aggregation and Sampling
Data Aggregation and Sampling
Basic aggregation
Enhanced aggregation
Aggregation condition
Window functions
Sampling
Summary
7
Performance Considerations
Performance Considerations
Performance utilities
Design optimization
Data optimization
Job optimization
Summary
8
Extensibility Considerations
Extensibility Considerations
User-defined functions
HPL/SQL
Streaming
SerDe
Summary
9
Security Considerations
Security Considerations
Authentication
Authorization
Mask and encryption
Summary
10
Working with Other Tools
Working with Other Tools
The JDBC/ODBC connector
NoSQL
The Hue/Ambari Hive view
HCatalog
Oozie
Spark
Hivemall
Summary
11
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Data Definition Language

Hive's Data Definition Language (DDL) is a subset of HQL statements that describe the Hive data structure by creating, deleting, or altering schema objects such as databases, tables, views, partitions, and buckets. Most DDL statements start with the CREATE, DROP, or ALTER keywords. The syntax of HQL DDL is very similar to SQL DDL. In the next section, we'll focus on the details of HQL DDL.

HQL uses -- before a single line of characters as comments, and it does not support multiline comments until v2.3.0. After v2.3.0, we can use bracketed single or multiline comments between /* and */.
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