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Apache Hive Essentials - Second Edition

By : Dayong Du

4 (2)

Buy this Book

Apache Hive Essentials

4 (2)

By: Dayong Du

Buy this Book

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

Preface

Who this book is for

What this book covers

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Free Chapter

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

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

Data Definition and Description

Understanding data types

Data type conversions

Data Definition Language

Database

Tables

Partitions

Buckets

Views

Summary

Data Correlation and Scope

Project data with SELECT

Filtering data with conditions

Linking data with JOIN

Combining data with UNION

Summary

Data Manipulation

Data exchanging with LOAD

Data exchange with INSERT

Data exchange with [EX|IM]PORT

Data sorting

Functions

Transactions and locks

Summary

Data Aggregation and Sampling

Basic aggregation

Enhanced aggregation

Aggregation condition

Window functions

Sampling

Summary

Performance Considerations

Performance utilities

Design optimization

Data optimization

Job optimization

Summary

Extensibility Considerations

User-defined functions

HPL/SQL

Streaming

SerDe

Summary

Security Considerations

Authentication

Authorization

Mask and encryption

Summary

Working with Other Tools

The JDBC/ODBC connector

NoSQL

The Hue/Ambari Hive view

HCatalog

Oozie

Spark

Hivemall

Summary

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Data optimization

Data file optimization covers the performance improvement on the data files in terms of file format, compression, and storage.

File format

Hive supports TEXTFILE, SEQUENCEFILE, AVRO, RCFILE, ORC, and PARQUET file formats. There are two HQL statements used to specify the file format as follows:

CREATE TABLE ... STORE AS <file_format>: Specify the file format when creating a table
ALTER TABLE ... [PARTITION partition_spec] SET FILEFORMAT <file_format>: Modify the file format (definition only) in an existing table

Once a table stored in text format is created, we can load text data directly into it. To load text data into tables that have other file formats, we can first load the data into a table...

Tech Concepts

Programming languages

Tech Tools