In this video, learn about Gemfire, LDAP, Solr, Redis, and the community projects.
- [Narrator] Let's take a look at other data sources that are supported by Pivotal under the Spring Data Umbrella Project. They all support the repository abstraction, and property expression queries found in Spring Data Commons, as well as method-specific to the data source. First, there is Spring Data GemFire. Spring Data GemFire is a project that makes it easier to build scalable, Spring-enabled distributed data management applications that use GemFire. To learn more about GemFire and in-memory data grid for real-time high-performant applications, see the GemFire website under pivotal.io Next is Spring Data Key Value.
Spring Data Key Value is a project that provides infrastructure to implement Spring data repositories on top of a key value-based in-memory data store. It abstracts away from a particular key value data source such as Redis, however, if you need to tap into more of the Redis features, Spring Data Redis is available. It provides high and low-level abstractions to easily tap into Redis features. Redis is an in-memory data structure store. It can be used as a database, cache, and message broker.
Their website is under redis.io And there is a Spring Data LDAP. Applications use the lightweight directory access protocol to access object directory mapping data sources. The data source could be Microsoft Active Directory or Linux LDAP, for example. The next module supports the Cassandra NoSQL Database. Spring Data for Apache Cassandra offers Spring Data JPA developers and easier entry into the paradigm shift away from relational databases.
Cassandra is a distributed database management system designed to handle large amounts of data across many servers. It supports data center, spanning, clusters with asynchronous replication. More can be found at the cassandra.apache.org site. Spring Data for Apache Solr provides easy configuration and access to the Apache Solr search server. Solr is a full-text search server that uses Lucene search library as its core for full-text indexing and search via RESTful JSON APIs.
It provides distributed search and index replication that is designed to be scalable and fault-tolerant. The list of data sources that leverage Spring Data continues to grow. On the projects.spring.io/spring-data page, there's even a section called community modules. There are Spring Data solutions implemented by developers and industries in the community. The source and documentation for each module is hosted on GitHub, like Spring Data Aerospike, others like Spring Data Couchbase, have their own Spring Data page that links out to GitHub.
Have you ever used Spring's JDBC template for relational databases? This convenient abstraction from JDBC lost some popularity over the years after the advent of ORM frameworks, but it is still available and has upgrades. It is now part of the Spring Data JDBC Extensions module, which includes advanced access to Oracle databases, and even supports Query DSL search criteria. And finally, there is deep support for Apache Hadoop. While not based on repository abstractions and comments, Spring Data for Apache Hadoop, does provide a rich set of features to interact with Hadoop, as well as the Spring integration and Spring batch frameworks, to address a wide range of use cases.
In this course, learn how to easily implement JPA-based repositories using Spring Data JPA. Mary Ellen Bowman describes the Spring Data umbrella project, and helps you understand JPA for object-relational mapping. She also covers querying, and dives into other Spring Data Commons features such as QueryDSL and auditing.
- Spring Data Commons
- Using JPA for object-relational mapping
- Declaring Spring Data Repositories
- Creating query methods with property expressions and @Query
- Query by example
- QueryDSL Spring Data Extension
- Spring Data REST
- Introduction to Spring Data Mongo
- Common pitfalls