Data Access 2.0? Please welcome, Spring Data!
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This document discusses Spring Data, a framework that provides a consistent programming model for data access while supporting different data store technologies. It summarizes that Spring Data aims to abstract custom traits of different data stores without over-abstracting. It also highlights key Spring Data features like mapping, templates, repositories, and support for both relational and non-relational databases.
Data Access 2.0? Please welcome, Spring Data!
- 1. Data Access 2.0? …please welcome… Spring Data! Oliver Gierke
- 4. How? Why? What?
- 6. What to expect? NOT!
- 7. What to expect? NOT! 7
- 8. Retrospect
- 10. Scaling
- 11. Data structures
- 12. Hibari Voldemort Membase Riak Cassandra Redis SimpleDB (No)SQL MongoDB OrientDB CouchDB HBase Sones Neo4J
- 13. Graphs
- 14. Documents
- 15. Column families
- 16. Key Value
- 17. Forest for the woods?
- 19. There‘s some Spring for that!
- 20. Spring Data
- 21. " … provide a familiar and consistent Spring-based programming model while not over-abstracting custom traits of the specific store.
- 22. Spring Data JDBC JPA
- 23. Spring Data JDBC JPA
- 24. Spring Data JDBC JPA
- 25. Spring Data JDBC JPA
- 26. Spring Data JDBC JPA
- 27. Building blocks
- 28. Spring
- 29. Mapping
- 30. Templates
- 31. Repositories
- 32. Repositories Querydsl 32
- 33. DEMO
- 34. Wrap up
- 35. Wrap up • Sophisticated mapping support • Templates • Repositories • Querydsl • Spring namespace • Geospatial support • Cross-store persistence
- 36. Questions?
- 37. Resources • www.springframework.org/spring-data • github.com/SpringSource/spring-data-mongodb • http://www.se-radio.net/2010/07/episode-165-nosql-and- mongodb-with-dwight-merriman • http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs- redis