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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
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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
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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
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Other Books You May Enjoy
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Other Books You May Enjoy
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Design optimization

Design optimization covers several data layout and design strategies to improve performance.

Partition tables

Hive partitioning is one of the most effective methods to improve the query performance on larger tables. The query with partition filtering will only load the data in the specified partitions (subdirectories), so it can execute much faster than a normal query that filters by a non-partitioning field. The selection of partition key is always an important factor for performance. It should always be a low cardinal attribute to avoid many subdirectories overhead.

The following are some commonly used dimensions as partition keys:

  • Partitions by date and time: Use date and time, such as year, month, and day (even hours), as partition keys when data is associated with the time dimension

  • Partitions by locations: Use country, territory, state, and city as partition keys when data is location related

  • Partitions by business logics: Use department, sales region, applications...

CONTINUE READING
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Tech Concepts
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Programming languages
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Apache Hive Essentials
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