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Spring Databy Jon Brisbin
Synopses & Reviews
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, youll 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. Youll also discover the features Spring Data adds to Springs existing JPA and JDBC support for writing RDBMS-based data access layers.
As a developer of Java enterprise applications, you can choose among several data access frameworks when working with relational databases. But what do you turn to when working with newer technologies such as NoSQL databases and Hadoop? This book shows you how Springs data access framework can help you connect to either non-relational or relational databases, or a combination of the two.
Youll learn how Spring Datas familiar and consistent programming model greatly reduces the learning curve for creating applications with newer data access technologies. And youll discover how to use Spring Datas improved JPA and JDBC support to increase your productivity when writing RDBMS-based data access layers.
Relational database technologies continue to be predominant in the enterprise, but theyre no longer considered a "one size fits all" solution. This book shows you how to increase your options.
About the Author
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 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 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.
Jon Brisbin is a member of the SpringSource Spring Data team and focuses on providing developers useful libraries to facilitate next-generation data manipulation. He's helped bring elements of the Grails GORM object mapper to Java-based MongoDB applications, he's provided key integration components between the Riak datastore and the RabbitMQ message broker, he blogs and speaks on evented application models, and is working diligently to bridge the gap between the bleeding-edge non-blocking and traditional JVM-based applications.
Michael Hunger has been passionate about software development for a long time. He is particularly interested in the people who develop software, software craftsmanship, programming languages, and improving code.
Table of Contents
ForewordPrefaceBackgroundChapter 1: The Spring Data ProjectChapter 2: Repositories: Convenient Data Access LayersChapter 3: Type-Safe Querying Using QuerydslRelational DatabasesChapter 4: JPA RepositoriesChapter 5: Type-Safe JDBC Programming with Querydsl SQLNoSQLChapter 6: MongoDB: A Document StoreChapter 7: Neo4j: A Graph DatabaseChapter 8: Redis: A Key/Value StoreRapid Application DevelopmentChapter 9: Persistence Layers with Spring RooChapter 10: REST Repository ExporterBig DataChapter 11: Spring for Apache HadoopChapter 12: Analyzing Data with HadoopChapter 13: Creating Big Data Pipelines with Spring Batch and Spring IntegrationData GridsChapter 14: GemFire: A Distributed Data GridBibliographyColophon
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