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  • Book Overview & Buying Apache Hive Essentials
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Apache Hive Essentials

Apache Hive Essentials

By : Dayong Du
4 (2)
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Apache Hive Essentials

Apache Hive Essentials

4 (2)
By: Dayong Du
Buy this Book

Overview of this book

If you are a data analyst, developer, or simply someone who wants to use Hive to explore and analyze data in Hadoop, this is the book for you. Whether you are new to big data or an expert, with this book, you will be able to master both the basic and the advanced features of Hive. Since Hive is an SQL-like language, some previous experience with the SQL language and databases is useful to have a better understanding of this book.
Table of Contents (12 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Overview of Big Data and Hive
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Overview of Big Data and Hive
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A short history
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Introducing big data
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The relational and NoSQL databases versus Hadoop
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Batch, real-time, and stream processing
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Overview of the Hadoop ecosystem
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Hive overview
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Summary
2
Setting Up the Hive Environment
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Setting Up the Hive Environment
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Installing Hive from Apache
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Installing Hive from vendors
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Using Hive in the cloud
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Using the Hive command
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Using the Hive IDE
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Summary
3
Data Definition and Description
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Data Definition and Description
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Understanding data types
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Data type conversions
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Data Definition Language
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Database
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Tables
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Partitions
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Buckets
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Views
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Summary
4
Data Correlation and Scope
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Data Correlation and Scope
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Project data with SELECT
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Filtering data with conditions
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Linking data with JOIN
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Combining data with UNION
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Summary
5
Data Manipulation
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Data Manipulation
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Data exchanging with LOAD
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Data exchange with INSERT
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Data exchange with [EX|IM]PORT
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Data sorting
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Functions
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Transactions and locks
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Summary
6
Data Aggregation and Sampling
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Data Aggregation and Sampling
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Basic aggregation
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Enhanced aggregation
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Aggregation condition
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Window functions
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Sampling
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Summary
7
Performance Considerations
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Performance Considerations
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Performance utilities
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Design optimization
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Data optimization
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Job optimization
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Summary
8
Extensibility Considerations
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Extensibility Considerations
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User-defined functions
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HPL/SQL
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Streaming
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SerDe
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Summary
9
Security Considerations
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Security Considerations
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Authentication
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Authorization
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Mask and encryption
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Summary
10
Working with Other Tools
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Working with Other Tools
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The JDBC/ODBC connector
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NoSQL
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The Hue/Ambari Hive view
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HCatalog
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Oozie
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Spark
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Hivemall
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Summary
11
Other Books You May Enjoy
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Other Books You May Enjoy
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Leave a review - let other readers know what you think

Understanding Hive data types

Hive data types are categorized into two types: primitive and complex data types. String and integer are the most useful primitive types, which are supported by most Hive functions.

Tip

Downloading the example code

You can download the example code files from your account at http://www.packtpub.com for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you

The details of primitive types are as follows:

Primitive data type

Description

Example

TINYINT

It has 1 byte from -128 to 127. The postfix is Y. It is used as a small range of numbers.

10Y

SMALLINT

It has 2 bytes from -32,768 to 32,767. The postfix is S. It is used as a regular descriptive number.

10S

INT

It has 4 bytes from -2,147,483,648 to 2,147,483,647.

10

BIGINT

It has 8 bytes from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807. The postfix...

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