Hibernate vs Spring Data vs jOOQ - Understanding Java Persistence

Hibernate vs Spring Data vs jOOQ - Understanding Java Persistence

IntelliJ IDEA by JetBrains via YouTube Direct link

59:02 Common database performance issues

24 of 37

24 of 37

59:02 Common database performance issues

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Hibernate vs Spring Data vs jOOQ - Understanding Java Persistence

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  1. 1 00:00 Introduction: From painful early learning to AI-generated annotations
  2. 2 01:02 Meet Thorben Janssen – consultant and trainer for Java persistence
  3. 3 02:11 Early days of Hibernate and the horror of EJB2
  4. 4 04:33 Main challenges in database access today
  5. 5 06:18 Too many tools: Hibernate, JPA, Spring Data, jOOQ – how to choose
  6. 6 08:15 Why understanding ORM internals really matters
  7. 7 09:20 How juniors should start learning persistence
  8. 8 10:59 SQL skills – why you still need them even with Hibernate or Spring Data
  9. 9 13:48 SQL essentials for beginners: what to learn first
  10. 10 16:01 Why Hibernate became the dominant persistence tool
  11. 11 18:57 Hibernate vs JPA – is there really a difference?
  12. 12 20:35 Hibernate API vs Jakarta Persistence API – which to use
  13. 13 22:10 Criteria API and DSLs for queries
  14. 14 23:43 Strengths and weaknesses of Spring Data JPA
  15. 15 27:26 Jakarta Data vs Spring Data JPA
  16. 16 30:53 Stateful vs stateless data models
  17. 17 33:21 jOOQ, Exposed, and the SQL-centric approach
  18. 18 37:41 Mixing Hibernate and Exposed in one project
  19. 19 39:33 Where Spring Data JDBC fits in
  20. 20 43:24 Starting a new project in 2025 – what stack to choose
  21. 21 45:24 The role of experience and not chasing every new trend
  22. 22 48:50 Monoliths, microservices, and common pitfalls
  23. 23 52:14 Database design vs service design – where to start
  24. 24 59:02 Common database performance issues
  25. 25 01:03:10 Why developers should collaborate with DBAs
  26. 26 01:06:20 Caching problems: when caches make things worse
  27. 27 01:09:29 Reactive database access – what happened to the hype
  28. 28 01:12:33 The “perfect” persistence framework – does it exist?
  29. 29 01:14:40 AI-assisted development and query generation
  30. 30 01:19:43 The AI hype cycle and developer reality
  31. 31 01:20:17 Other persistence models worth learning graph, full-text search
  32. 32 01:21:43 Full-text search and graph databases in practice
  33. 33 01:23:25 Integrating AI into applications
  34. 34 01:24:38 Thorben’s unpopular opinions
  35. 35 01:26:04 Giveaway announcement
  36. 36 01:27:06 Rapid-fire round: Paderborn, joins, and lazy loading
  37. 37 01:30:00 Closing remarks

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