First Milestone of Spring Data Release Train Babbage includes Spring Data Neo4j 2.3 M1

Most of the changes of this release have made it into Spring Data Commons to build a solid foundation for the next generation of Spring Data projects and make sure that foundation matures fastly. The other modules released in this train station have been adapted to these changes and thus benefit from them as well. We’ve upgraded to Querydsl 3.x APIs to accomodate the changes introduced in their major release. The repositories abstraction has added support for ordering ignoring case as well as count…By…(…) projection for derived queries. We also gave the mapping metadata implementation a serious performance overhaul so that especially the MongoDB and Neo4j modules should see a ~20% performance increase for mapping operations. Another big chunk of work went into the overhaul of the pagination and web support, especially in combination with Spring HATEOAS. Creating paginated resource representations for you Spring MVC controllers has never been easier, as you can see in the reference documentation. The changes in Spring Data Commons are rounded off by some improvements in the CDI integration as well as the move of the ChainedTransactionManager from Spring Data Neo4j into the core module. In Spring Data MongoDB we added support for customizing the field names through a global strategy and ship a CamelCaseAbbreviatingFieldNamingStrategy out of the box. We’ve introduced XML namespace elements for MongoTemplate and GridFsTemplate, added support for the background attributes for indexing and now also support DBRefs in Map values. The Neo4j module brings updates to the latest Neo4j and Cypher releases. Read the full article. H Online

H-Online reports: Spring Batch plugs into Neo4j and MongoDB

Spring Data support and Java configuration are highlights of the new Spring Batch release from Pivotal’s SpringSource division. Spring Batch is a lightweight framework for developing batch applications and builds upon the Spring framework’s development approach. It is designed to address the need for periodically executed business critical tasks. The new 2.2.0 version of Spring Batch follows in the footsteps of other Spring projects which are integrating NoSQL databases and other “big data” sources using the Spring Data project and moving over to a programmatic configuration model rather than an XML-based one. Read the full article.