Instant Apache Hive Essentials How-to [Instant]
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- Learn something new in an Instant! A short, fast, focused guide delivering immediate results
- Learn to use SQL to write Hadoop jobs
- Add support for data to Hive in your own file formats
- Understand how the Hive query processor works to optimize common queries
Book DetailsLanguage : English
eBook : 76 pages
Release Date : June 2013
ISBN : 1782169474
ISBN 13 : 9781782169475
Author(s) : Darren Lee
Topics and Technologies : All Books, Big Data and Business Intelligence, Instant, Open Source
Table of ContentsPreface
Instant Apache Hive Essentials How-to
- Instant Apache Hive Essentials How-to
- Tables and queries (Simple)
- Understanding complex data types (Simple)
- Using Hive non-interactively (Simple)
- Join optimizations (Medium)
- Setting the file format (Simple)
- Writing a custom SerDe (Intermediate)
- Using static partitions (Intermediate)
- Using dynamic partitions (Intermediate)
- Using functions (Simple)
- Adding custom logic with streaming (Intermediate)
- Simple user-defined functions (Intermediate)
- Advanced user-defined functions (Advanced)
- User-defined table-generating functions (Advanced)
- User-defined aggregation functions (Advanced)
Download the code and support files for this book.
Please let us know if you have found any errors not listed on this list by completing our errata submission form. Our editors will check them and add them to this list. Thank you.
Errata- 1 submitted: last submission 25 Oct 2013
Errata type: Code | Page number: 9
Code in Bullet point 6
> num_bidsint) ;
Should be: > num_bids int) ;
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What you will learn from this book
- Start with the basics of loading data and writing your first query
- Use de-normalized data efficiently by manipulating complex data types
- Structure your data and queries to take advantage of Hive’s optimizations
- Bring your own data files to Hive and teach Hive how to understand them
- Access the specialized functions built-in to Hive to manipulate your data
- Use Hive streaming to integrate code written in any language into your Extend Hive with user-defined functions
Hadoop provides a robust framework for building distributed applications, but working directly with Hadoop requires writing a lot of code. Adding structure to data and using a higher-level language such as SQL makes working with Hadoop both easier and faster.
"Instant Apache Hive Essentials How-to" contains a series of practical recipes that introduce the power and flexibility of Hive. Starting with your first query, this book will provide step-by-step instructions and behind-the-scenes explanations for how to effectively write MapReduce jobs with SQL.
This book looks at how Hive transforms SQL statements into MapReduce jobs and demonstrates how you can extend Hive to support your own use cases. Its recipes will teach you how to leverage the scale of Hadoop while retaining the benefits of using a structured query language.You will learn how Hive translates a query into MapReduce jobs and explore how to structure your queries for better performance. You will extend Hive to understand your own file formats, simplifying the loading of data into the warehouse. You will finally add your own custom functions to Hive to support whatever use cases you may have.
"Instant Apache Hive Essentials How-to" is a quick introduction for adding Hive to your data toolkit. It is packed with high-level instructions for making Hive work as well as drawing connections to the underlying Hadoop framework to explain how things happen.
Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks.This book provides quick recipes for using Hive to read data in various formats, efficiently querying this data, and extending Hive with any custom functions you may need to insert your own logic into the data pipeline.
Who this book is for
This book is written for data analysts and developers who want to use their current knowledge of SQL to be more productive with Hadoop. It assumes that readers are comfortable writing SQL queries and are familiar with Hadoop at the level of the classic WordCount example.