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Dive into the world of SQL on Hadoop and get the most out of your Hive data warehouses. This book is your go-to resource for using Hive: authors Scott Shaw, Ankur Gupta, David Kjerrumgaard, and Andreas Francois Vermeulen take you through learning HiveQL, the SQL-like language specific to Hive, to analyze, export, and massage the data stored across your Hadoop environment. From deploying Hive on your hardware or virtual machine and setting up its initial configuration to learning how Hive interacts with Hadoop, MapReduce, Tez and other big data technologies, Practical Hive gives you a detailed treatment of the software.

In addition, this book discusses the value of open source software, Hive performance tuning, and how to leverage semi-structured and unstructured data. 

What You Will Learn

• Install and configure Hive for new and existing datasets
  
• Perform DDL operations
  
• Execute efficient DML operations
• Use tables, partitions, buckets, and user-defined functions
• Discover performance tuning tips and Hive best practices
  

Who This Book Is For

Developers, companies, and professionals who deal with large amounts of data and could use software that can efficiently manage large volumes of input. It is assumed that readers have the ability to work with SQL. 

Practical Hive: A Guide to Hadoop's Data Warehouse System

Chapter 1: Setting  the Stage for Hive: Hadoop.- Chapter 2: Introducing Hive.- Chapter 3: Hive Architecture.- Chapter 4: Hive Tables DDL.- Chapter 5: Data Manipulation Language (DML).- Chapter 6: Loading Data into Hive.- Chapter 7: Querying Semi-Structured Data.- Chapter 8: Hive Analytics.- Chapter 9: Performance Tuning: Hive.- Chapter 10: Hive Security.- Chapter 11: Future of Hive.- Chapter 12: Appendix A. Building a Big Data Team.- Chapter 13: Appendix B. Hive Functions.