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Spring Data: The Definitive Guide

   
지은이   |   출판사 O'Reilly Media  |   발행일 2012년 11월 03일   |   언어 : English
 
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발행일 2012-11-03 | 중량: 0.51 kg
ISBN 1449323952 | 9781449323950
기타정보 원서 | 316쪽 | $ 39.99 | Paperback
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    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
    Background
    Chapter 1 : The Spring Data Project
    NoSQL Data Access for Spring Developers
    General Themes
    The Domain
    The Sample Code
    Chapter 2 : Repositories: Convenient Data Access Layers
    Quick Start
    Defining Query Methods
    Defining Repositories
    IDE Integration
    Chapter 3 : Type-Safe Querying Using Querydsl
    Introduction to Querydsl
    Generating the Query Metamodel
    Integration with Spring Data Repositories
    Relational Databases
    Chapter 4 : JPA Repositories
    The Sample Project
    The Traditional Approach
    Bootstrapping the Sample Code
    Using Spring Data Repositories
    Chapter 5 : Type-Safe JDBC Programming with Querydsl SQL
    The Sample Project and Setup
    The QueryDslJdbcTemplate
    Executing Queries
    Insert, Update, and Delete Operations
    NoSQL
    Chapter 6 : MongoDB: A Document Store
    MongoDB in a Nutshell
    Setting Up the Infrastructure Using the Spring Namespace
    The Mapping Subsystem
    MongoTemplate
    Mongo Repositories
    Chapter 7 : Neo4j: A Graph Database
    Graph Databases
    Neo4j
    Spring Data Neo4j Overview
    Modeling the Domain as a Graph
    Persisting Domain Objects with Spring Data Neo4j
    Combining Graph and Repository Power
    Advanced Graph Use Cases in the Example Domain
    Transactions, Entity Life Cycle, and Fetch Strategies
    Advanced Mapping Mode
    Working with Neo4j Server
    Continuing From Here
    Chapter 8 : Redis: A Key/Value Store
    Redis in a Nutshell
    Connecting to Redis
    Object Conversion
    Object Mapping
    Atomic Counters
    Pub/Sub Functionality
    Using Spring’s Cache Abstraction with Redis
    Rapid Application Development
    Chapter 9 : Persistence Layers with Spring Roo
    A Brief Introduction to Roo
    Roo’s Persistence Layers
    Quick Start
    A Spring Roo JPA Repository Example
    A Spring Roo MongoDB Repository Example
    Chapter 10 : REST Repository Exporter
    The Sample Project
    Big Data
    Chapter 11 : Spring for Apache Hadoop
    Challenges Developing with Hadoop
    Hello World
    Hello World Revealed
    Hello World Using Spring for Apache Hadoop
    Scripting HDFS on the JVM
    Combining HDFS Scripting and Job Submission
    Job Scheduling
    Chapter 12 : Analyzing Data with Hadoop
    Using Hive
    Using Pig
    Using HBase
    Chapter 13 : Creating Big Data Pipelines with Spring Batch and Spring Integration
    Collecting and Loading Data into HDFS
    Hadoop Workflows
    Exporting Data from HDFS
    Collecting and Loading Data into Splunk
    Data Grids
    Chapter 14 : GemFire: A Distributed Data Grid
    GemFire in a Nutshell
    Caches and Regions
    How to Get GemFire
    Configuring GemFire with the Spring XML Namespace
    Data Access with GemfireTemplate
    Repository Usage
    Continuous Query Support
    Mark Pollack
    Dr. Mark Pollack has worked on Big Data solutions in High Energy Physics at Brookhaven National Laboratory and then moved to the financial services industry as a technical lead or architect for front office trading systems.Always interested in best practices and improving the software development process, Mark has been a core Spring (Java) developer since 2003 and founded its Microsoft counterpart, Spring.NET, in 2004.Mark now leads the Spring Data project that aims to simplify application development with new data technologies around Big Data and NoSQL databases.

    Oliver Gierke
    Oliver Gierke is engineer at SpringSource, a division of VMware, project lead of the Spring Data JPA, MongoDB and core module. He has been into developing enterprise applications and open source projects for over 6 years now. His working focus is centered around software architecture, Spring and persistence technologies. He is regularly speaking at German and international conferences as well as author of technology articles.

    Thomas Risberg
    Thomas Risberg is currently a member of the Spring Data team focusing on the MongoDB and JDBC Extensions projects. He is also a committer on the Spring Framework project, primarily contributing to enhancements of the JDBC framework portion.

    Thomas works on the VMware's Cloud Foundry team developing integration for the various frameworks and languages supported by the Cloud Foundry project.

    Thomas is co-author of “Professional Java Development with the Spring Framework” together with Rod Johnson, Juergen Hoeller, Alef Arendsen, and Colin Sampaleanu, published by Wiley in 2005.
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