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.

Dayong Du is a big data practitioner, leader, and developer with expertise in technology consulting, designing, and implementing enterprise big data solutions. With more than 10 years of experience in enterprise data warehouse, business intelligence, and big data and analytics, he has provided his data intelligence expertise in various industries, such as media, travel, telecommunications, and so on. He is currently working with QuickPlay Media in Toronto, Canada, to build enterprise big data intelligence reporting for online media services and content providers. He has a master's degree in computer science from Dalhousie University, and he holds the Cloudera Certified Developer for Apache Hadoop certification.

Table of Contents
Apache Hive Essentials
Credits
About the Author
About the Reviewers
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Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Overview of Big Data and Hive
A short history
Introducing big data
Relational and NoSQL database versus Hadoop
Batch, real-time, and stream processing
Overview of the Hadoop ecosystem
Hive overview
Summary
2. Setting Up the Hive Environment
Installing Hive from Apache
Installing Hive from vendor packages
Starting Hive in the cloud
Using the Hive command line and Beeline
The Hive-integrated development environment
Summary
3. Data Definition and Description
Understanding Hive data types
Data type conversions
Hive Data Definition Language
Hive database
Hive internal and external tables
Hive partitions
Hive buckets
Hive views
Summary
4. Data Selection and Scope
The SELECT statement
The INNER JOIN statement
The OUTER JOIN and CROSS JOIN statements
Special JOIN – MAPJOIN
Set operation – UNION ALL
Summary
5. Data Manipulation
Data exchange – LOAD
Data exchange – INSERT
Data exchange – EXPORT and IMPORT
ORDER and SORT
Operators and functions
Transactions
Summary
6. Data Aggregation and Sampling
Basic aggregation – GROUP BY
Advanced aggregation – GROUPING SETS
Advanced aggregation – ROLLUP and CUBE
Aggregation condition – HAVING
Analytic functions
Sampling
Summary
7. Performance Considerations
Performance utilities
The EXPLAIN statement
The ANALYZE statement
Design optimization
Partition tables
Bucket tables
Index
Data file optimization
File format
Compression
Storage optimization
Job and query optimization
Local mode
JVM reuse
Parallel execution
Join optimization
Common join
Map join
Bucket map join
Sort merge bucket (SMB) join
Sort merge bucket map (SMBM) join
Skew join
Summary
8. Extensibility Considerations
User-defined functions
The UDF code template
The UDAF code template
The UDTF code template
Development and deployment
Streaming
SerDe
Summary
9. Security Considerations
Authentication
Metastore server authentication
HiveServer2 authentication
Authorization
Legacy mode
Storage-based mode
SQL standard-based mode
Encryption
Summary
10. Working with Other Tools
JDBC / ODBC connector
HBase
Hue
HCatalog
ZooKeeper
Oozie
Hive roadmap
Summary
Index