Spring Data

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

Foreword;
Preface;
Overview of the New Data Access Landscape;
How to Read This Book;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Acknowledgments;
Background;
Chapter 1: The Spring Data Project;
1. 1 NoSQL Data Access for Spring Developers;
1. 2 General Themes;
1. 3 The Domain;
1. 4 The Sample Code;
Chapter 2: Repositories: Convenient Data Access Layers;
2. 1 Quick Start;
2. 2 Defining Query Methods;
2. 3 Defining Repositories;
2. 4 IDE Integration;
Chapter 3: Type-Safe Querying Using Querydsl;
3. 1 Introduction to Querydsl;
3. 2 Generating the Query Metamodel;
3. 3 Integration with Spring Data Repositories;
Relational Databases;
Chapter 4: JPA Repositories;
4. 1 The Sample Project;
4. 2 The Traditional Approach;
4. 3 Bootstrapping the Sample Code;
4. 4 Using Spring Data Repositories;
Chapter 5: Type-Safe JDBC Programming with Querydsl SQL;
5. 1 The Sample Project and Setup;
5. 2 The QueryDslJdbcTemplate;
5. 3 Executing Queries;
5. 4 Insert, Update, and Delete Operations;
NoSQL;
Chapter 6: MongoDB: A Document Store;
6. 1 MongoDB in a Nutshell;
6. 2 Setting Up the Infrastructure Using the Spring Namespace;
6. 3 The Mapping Subsystem;
6. 4 MongoTemplate;
6. 5 Mongo Repositories;
Chapter 7: Neo4j: A Graph Database;
7. 1 Graph Databases;
7. 2 Neo4j;
7. 3 Spring Data Neo4j Overview;
7. 4 Modeling the Domain as a Graph;
7. 5 Persisting Domain Objects with Spring Data Neo4j;
7. 6 Combining Graph and Repository Power;
7. 7 Advanced Graph Use Cases in the Example Domain;
7. 8 Transactions, Entity Life Cycle, and Fetch Strategies;
7. 9 Advanced Mapping Mode;
7. 10 Working with Neo4j Server;
7. 11 Continuing From Here;
Chapter 8: Redis: A Key/Value Store;
8. 1 Redis in a Nutshell;
8. 2 Connecting to Redis;
8. 3 Object Conversion;
8. 4 Object Mapping;
8. 5 Atomic Counters;
8. 6 Pub/Sub Functionality;
8. 7 Using Spring's Cache Abstraction with Redis;
Rapid Application Development;
Chapter 9: Persistence Layers with Spring Roo;
9. 1 A Brief Introduction to Roo;
9. 2 Roo's Persistence Layers;
9. 3 Quick Start;
9. 4 A Spring Roo JPA Repository Example;
9. 5 A Spring Roo MongoDB Repository Example;
Chapter 10: REST Repository Exporter;
10. 1 The Sample Project;
Big Data;
Chapter 11: Spring for Apache Hadoop;
11. 1 Challenges Developing with Hadoop;
11. 2 Hello World;
11. 3 Hello World Revealed;
11. 4 Hello World Using Spring for Apache Hadoop;
11. 5 Scripting HDFS on the JVM;
11. 6 Combining HDFS Scripting and Job Submission;
11. 7 Job Scheduling;
Chapter 12: Analyzing Data with Hadoop;
12. 1 Using Hive;
12. 2 Using Pig;
12. 3 Using HBase;
Chapter 13: Creating Big Data Pipelines with Spring Batch and Spring Integration;
13. 1 Collecting and Loading Data into HDFS;
13. 2 Hadoop Workflows;
13. 3 Exporting Data from HDFS;
13. 4 Collecting and Loading Data into Splunk;
Data Grids;
Chapter 14: GemFire: A Distributed Data Grid;
14. 1 GemFire in a Nutshell;
14. 2 Caches and Regions;
14. 3 How to Get GemFire;
14. 4 Configuring GemFire with the Spring XML Namespace;
14. 5 Data Access with GemfireTemplate;
14. 6 Repository Usage;
14. 7 Continuous Query Support;
Bibliography;
Colophon;