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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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Advanced aggregation – ROLLUP and CUBE

The ROLLUP statement enables a SELECT statement to calculate multiple levels of aggregations across a specified group of dimensions. The ROLLUP statement is a simple extension to the GROUP BY clause with high efficiency and minimal overhead to a query. Compared to GROUPING SETS that creates specified levels of aggregations, ROLLUP creates n+1 levels of aggregations, where n is the number of grouping columns. First, it calculates the standard aggregate values specified in the GROUP BY clause. Then, it creates higher-level subtotals, moving from right to left through the list of combinations of grouping columns, as shown in the following example:

GROUP BY a,b,c WITH ROLLUP

This is equivalent to the following:

GROUP BY a,b,c GROUPING SETS ((a,b,c),(a,b),(a),())

The CUBE statement takes a specified set of grouping columns and creates aggregations for all of their possible combinations. If n columns are specified for CUBE, there will be 2n combinations of...

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Apache Hive Essentials
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