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Oreilly Spring Data



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#1 OFFLINE   unixsaint

unixsaint

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Posted 11 November 2012 - 04:05 PM

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Spring Data

O’R-lly Media (October 2012) | ISBN: 1449323952 | PDF + EPUB | 316 pages | 16.0 MB



You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring Data makes it relatively easy to build applications across a wide range of new data access technologies such as NoSQL and Hadoop.

Through several sample projects, you’ll learn how Spring Data provides a consistent programming model that retains NoSQL-specific features and capabilities, and helps you develop Hadoop applications across a wide range of use-cases such as data analysis, event stream processing, and workflow. You’ll also discover the features Spring Data adds to Spring’s existing JPA and JDBC support for writing RDBMS-based data access layers.

Learn about Spring’s template helper classes to simplify the use ofdatabase-specific functionality
Explore Spring Data’s repository abstraction and advanced query functionality
Use Spring Data with Redis (key/value store), HBase(column-family), MongoDB (document database), and Neo4j (graph database)
Discover the GemFire distributed data grid solution
Export Spring Data JPA-managed entities to the Web as RESTful web services
Simplify the development of HBase applications, using a lightweight object-mapping framework
Build example big-data pipelines with Spring Batch and Spring Integration

Table of Contents

Part I: Background
Chapter 1. The Spring Data Project
Chapter 2. Repositories: Convenient Data Access Layers
Chapter 3. Type-Safe Querying Using Querydsl

Part II: Relational Databases
Chapter 4. JPA Repositories
Chapter 5. Type-Safe JDBC Programming with Querydsl SQL

Part III: NoSQL
Chapter 6. MongoDB: A Document Store
Chapter 7. Neo4j: A Graph Database
Chapter 8. Redis: A Key/Value Store

Part IV: Rapid Application Development
Chapter 9. Persistence Layers with Spring Roo
Chapter 10. REST Repository Exporter

Part V: Big Data
Chapter 11. Spring for Apache Hadoop
Chapter 12. Analyzing Data with Hadoop
Chapter 13. Creating Big Data Pipelines with Spring Batch and Spring Integration

Part VI: Data Grids
Chapter 14. GemFire: A Distributed Data Grid

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