What's the difference between JPA and Spring Data JPA?

java

repository-pattern

jpa-configuration

spring-data-jpa

byAlex Kataev·Mar 12, 2025

The Java Persistence API (JPA) is a Java specification describing a strategy for managing relational data in Java applications. It aims to bridge the gap between Java objects and database tables through ORM (Object-Relational Mapping).

Spring Data JPA on the other hand, eases usage of JPA by abstracting and implementing redundant code. It is built on top of JPA and provides a streamlined API for data access that includes CRUD operations and automatic query generation by simply declaring method names.

An JPA Entity example:

@Entity
public class FunExampleEntity {
  @Id
  private Long id;
  // More fun fields here!
  // Do they spark joy? If not, reconsider them.
}

With Spring Data JPA, we can create a Repository like so:

public interface FunExampleEntityRepository extends JpaRepository<FunExampleEntity, Long>{
  // Consumer of your API: I want a method that gets me entities by a property!
  // Spring Data JPA: Say no more!
  List<FunExampleEntity> findByFunPropertyName(String funPropertyName);
}

With Spring Data JPA:

Code construction is simplified, it automatically implements common data access code for us
Repositories offer readily available CRUD operations
Query generation is derived from method names, eradicating the need to write redundant SQL

Diving deeper into Spring Data JPA

Embracing the repository pattern with Spring Data JPA

Spring Data JPA shines with its support for the Repository Pattern, central to Domain-Driven Design (DDD). It helps abstract the data layer, creating a separation which provides a more maintainable and focused codebase. Spring Data repository interfaces alsos provide ready-made CRUD operations, allowing developers to focus on business logic, instead of data handling.

Enhanced querying with method naming conventions

Query generation from method signatures in your repository interfaces is a salient feature of Spring Data JPA. It provides not only quick but also type-safe retrieval of entities, without the need to write redundant SQL or JPQL code.

For better performance or complex queries, Spring Data JPA supports Querydsl predicates, providing a type-safe way to deal with the task. Additionally, it has the provision to define JPQL or native SQL queries using the @Query annotation directly on the repository methods.

Dynamic data handling: Pagination and Queries

Spring Data JPA is also great for dealing with large datasets and providing an interface to paged results thereby, making your application more responsive. Pagination comes virtually out-of-the-box, with minor additional configuration involved.

Auditing and query validation, but transparently

For large-scale applications, transparency in CRUD operations is essential. Spring Data JPA allows for transparent auditing of operations, creating trails for tracing operations. Moreover, validation of queries is also supported to ensure effective crafting of your query methods.

Switching JPA providers freely

Spring Data JPA facilitates easy switching between different JPA providers due to its abstract implementation. You can switch from one JPA provider, like Hibernate, to another one such as EclipseLink with minor changes in your codebase.

Bringing it all together: Configuration

Finally, Spring Data JPA integrates well with both XML and JavaConfig configurations, making it easy to set up in almost any project environment whether it's annotation-based or XML-based configuration.

Spring Data JPA in Spring ecosystem

Spring Data JPA works in perfect harmony with other Spring components like Spring Security, Spring MVC and Spring Transaction Management. For instance, using the @Transactional annotation, it becomes straightforward to manage transactions, ensuring your data consistency is safe and sound.

Enhancing productivity: less code, more execution

Spring Data JPA is committed to boosting developer productivity by reducing boilerplate code via automatic repository implementation. Therefore, developers can focus more on implementation of business logic than troubling about data operation complexities.

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