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Programming Hive - Data Warehouse and Query Language for Hadoop
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Located in: Fremont, California, United States
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eBay item number:267305527337
Item specifics
- Condition
- Like New
- Seller Notes
- “No writing or highlights, really in like-new condition”
- Country of Origin
- United States
- Book Title
- Programming Hive : Data Warehouse and Query Language for Hadoop P
- Personalized
- No
- Features
- EX-LIBRARY
- ISBN
- 9781449319335
About this product
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1449319335
ISBN-13
9781449319335
eBay Product ID (ePID)
117152135
Product Key Features
Number of Pages
347 Pages
Publication Name
Programming Hive : Data Warehouse and Query Language for Hadoop
Language
English
Publication Year
2012
Subject
Data Modeling & Design, Programming Languages / Java, Computer Science, General, Databases / Data Warehousing
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
0.8 in
Item Weight
21.7 Oz
Item Length
9.2 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
Illustrated
Yes
Table Of Content
Preface Chapter 1: Introduction Chapter 2: Getting Started Chapter 3: Data Types and File Formats Chapter 4: HiveQL: Data Definition Chapter 5: HiveQL: Data Manipulation Chapter 6: HiveQL: Queries Chapter 7: HiveQL: Views Chapter 8: HiveQL: Indexes Chapter 9: Schema Design Chapter 10: Tuning Chapter 11: Other File Formats and Compression Chapter 12: Developing Chapter 13: Functions Chapter 14: Streaming Chapter 15: Customizing Hive File and Record Formats Chapter 16: Hive Thrift Service Chapter 17: Storage Handlers and NoSQL Chapter 18: Security Chapter 19: Locking Chapter 20: Hive Integration with Oozie Chapter 21: Hive and Amazon Web Services (AWS) Chapter 22: HCatalog Chapter 23: Case Studies Glossary References Colophon
Synopsis
Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoop's data warehouse infrastructure. You'll quickly learn how to use Hive's SQL dialect--HiveQL--to summarize, query, and analyze large datasets stored in Hadoop's distributed filesystem. This example-driven guide shows you how to set up and configure Hive in your environment, provides a detailed overview of Hadoop and MapReduce, and demonstrates how Hive works within the Hadoop ecosystem. You'll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data. Use Hive to create, alter, and drop databases, tables, views, functions, and indexes Customize data formats and storage options, from files to external databases Load and extract data from tables--and use queries, grouping, filtering, joining, and other conventional query methods Gain best practices for creating user defined functions (UDFs) Learn Hive patterns you should use and anti-patterns you should avoid Integrate Hive with other data processing programs Use storage handlers for NoSQL databases and other datastores Learn the pros and cons of running Hive on Amazon's Elastic MapReduce, Hive makes life much easier for developers who work with stored and managed data in Hadoop clusters, such as data warehouses. With this example-driven guide, you'll learn how to use the Hive infrastructure to provide data summarization, query, and analysis - particularly with HiveQL, the query language dialect of SQL., Hive makes life much easier for developers who work with stored and managed data in Hadoop clusters, such as data warehouses. With this example-driven guide, you'll learn how to use the Hive infrastructure to provide data summarization, query, and analysis - particularly with HiveQL, the query language dialect of SQL. You'll learn how to set up Hive in your environment and optimize its use, and how it interoperates with other tools, such as HBase. You'll also learn how to extend Hive with custom code written in Java or scripting languages. Ideal for developers with prior SQL experience, this book shows you how Hive simplifies many tasks that would be much harder to implement in the lower-level MapReduce API provided by Hadoop.
LC Classification Number
QA76.9.D37
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