🔏
Tech
  • 🟢App aspects
    • Software architecture
      • Caching
      • Anti-patterns
      • System X-ability
      • Coupling
      • Event driven architecture
        • Command Query Responsibility Segregation (CQRS)
        • Change Data Capture (CDC)
      • Distributed transactions
      • App dev notes
        • Architecture MVP
      • TEMP. Check list
      • Hexagonal arch
      • Communication
        • REST vs messaging
        • gRPC
        • WebSocket
      • Load balancers
      • Storage limits
      • Event storming
    • Authentication
    • Deployment strategy
  • Databases
    • Classification
    • DB migration tools
    • PostreSQL
    • Decision guidance
    • Index
      • Hash indexes
      • SSTable, LSM-Trees
      • B-Tree
      • Engines, internals
    • Performance
  • System design
    • Interview preparation
      • Plan
        • Instagram
        • Tinder
        • Digital wallet
        • Dropbox
        • Live video streaming
        • Uber
        • Whatsup
        • Tiktok
        • Twitter
        • Proximity service
    • Algorithms
    • Acronyms
  • 🟢Programming languages
    • Java
      • Features
        • Field hiding
        • HashCode() and Equals()
        • Reference types
        • Pass by value
        • Atomic variables
      • Types
      • IO / NIO
        • Java NIO
          • Buffer
          • Channel
        • Java IO: Streams
          • Input streams
            • BufferedInputStream
            • DataInputStream
            • ObjectInputStream
            • FilterInputStream
            • ByteArrayInputStream
        • Java IO: Pipes
        • Java IO: Byte & Char Arrays
        • Java IO: Input Parsing
          • PushbackReader
          • StreamTokenizer
          • LineNumberReader
          • PushbackInputStream
        • System.in, System.out, System.error
        • Java IO: Files
          • FileReader
          • FileWriter
          • FileOutputStream
          • FileInputStream
      • Multithreading
        • Thread liveness
        • False sharing
        • Actor model
        • Singleton
        • Future, CompletableFuture
        • Semaphore
      • Coursera: parallel programming
      • Coursera: concurrent programming
      • Serialization
      • JVM internals
      • Features track
        • Java 8
      • Distributed programming
      • Network
      • Patterns
        • Command
      • Garbage Collectors
        • GC Types
        • How GC works
        • Tools for GC
    • Kotlin
      • Scope functions
      • Inline value classes
      • Coroutines
      • Effective Kotlin
    • Javascript
      • Javascript vs Java
      • TypeScript
    • SQL
      • select for update
    • Python
      • __init.py__
  • OS components
    • Network
      • TCP/IP model
        • IP address in action
      • OSI model
  • 🟢Specifications
    • JAX-RS
    • REST
      • Multi part
  • 🟢Protocols
    • HTTP
    • OAuth 2.0
    • LDAP
    • SAML
  • 🟢Testing
    • Selenium anatomy
    • Testcafe
  • 🟢Tools
    • JDBC
      • Connection pool
    • Gradle
    • vim
    • git
    • IntelliJ Idea
    • Elastic search
    • Docker
    • Terraform
    • CDK
    • Argo CD
      • app-of-app setup
    • OpenTelemetry
    • Prometheus
    • Kafka
      • Consumer lag
  • 🟢CI
    • CircleCi
  • 🟢Platforms
    • AWS
      • VPC
      • EC2
      • RDS
      • S3
      • IAM
      • CloudWatch
      • CloudTrail
      • ELB
      • SNS
      • Route 53
      • CloudFront
      • Athena
      • EKS
    • Kubernetes
      • Networking
      • RBAC
      • Architecture
      • Pod
        • Resources
      • How to try
      • Kubectl
      • Service
      • Tooling
        • ArgoCD
        • Helm
        • Istio
    • GraalVM
    • Node.js
    • Camunda
      • Service tasks
      • Transactions
      • Performance
      • How it executes
  • 🟢Frameworks
    • Hibernate
      • JPA vs Spring Data
    • Micronaut
    • Spring
      • Security
      • JDBC, JPA, Hibernate
      • Transactions
      • Servlet containers, clients
  • 🟢Awesome
    • Нейробиология
    • Backend
      • System design
    • DevOps
    • Data
    • AI
    • Frontend
    • Mobile
    • Testing
    • Mac
    • Books & courses
      • Path: Java Concurrency
    • Algorithms
      • Competitive programming
    • Processes
    • Finance
    • Electronics
  • 🟢Electronics
    • Arduino
    • IoT
  • Artificial intelligence
    • Artificial Intelligence (AI)
  • 🚀Performance
    • BE
  • 📘Computer science
    • Data structures
      • Array
      • String
      • LinkedList
      • Tree
    • Algorithms
      • HowTo algorithms for interview
  • 🕸️Web dev (Frontend)
    • Trends
    • Web (to change)
  • 📈Data science
    • Time series
Powered by GitBook
On this page
  • JPA
  • Spring Data JPA

Was this helpful?

  1. Frameworks
  2. Hibernate

JPA vs Spring Data

PreviousHibernateNextMicronaut

Last updated 2 years ago

Was this helpful?

JPA

JPA is all about creating Objects which can map to the Database objects.

The application will use JPA specification to push or retrieve an object from the database, and underlying JPA implementations will take care of the low-level SQL queries. That is called Object Relational Mapping (ORM).

There are many JPA implementations like EclipseLink or Hibernate. With ORM, we use an API to query a particular database entity, and the result is provided in the form of a collection of java objects. Similarly, when we want to push data to the database, we need to populate Java objects and pass them to the API. The underlying ORM implementation is responsible for converting these API calls to the native SQL queries.

Spring Data JPA

is one of the many Spring Data projects, and it aims towards bringing consistency in accessing data for relational datastores. Many people consider Spring Data JPA is a JPA implementation. In reality, it is false. Spring Data JPA uses a default JPA Implementation called Hibernate. The default JPA implementation is configurable, and if we wish, we can use other implementations as well.

What is Spring Data JPA, if not a JPA Implementation? Speaking precisely, Spring Data JPA is an add-on for JPA. It provides a framework that works with JPA and provides a complete abstraction over the Data Access Layer. Spring Data JPA brings in the concept of JPA Repositories, a set of Interfaces that defines . The Repository and Entity Bean represent the DAO layer in the application. No need to write native queries anymore. Sometimes we need to write queries or part of queries, but those are JQL queries and not native database queries.

Spring Data JPA is a sub-project of Spring Data and provides an abstraction over the Data Access Layer using Java Persistence API and ORM implementations like Hibernate.

🟢
Spring Data JPA
query methods