Yet & Datomic Immutable Facts Mutated Our Stack

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" summary

Company uses Datomic for three years and reports concrete wins: MVP in under a month, tiny backups — 30MB for a couple hundred thousand statements — and productive idempotent transactions by declaring uniqueness. Datomic ideas drive front-end event sourcing with Datascript/Re-frame, concise business logic in Datalog rules, and an ephemeral in-memory ETL workflow (Gorilla REPL, Hickory, dr, Incanter/core.matrix). At scale the stack moves to Kafka/Kafka Streams with Datomic used as a materialized view for complex queries.

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" chapters (53)

1. 0:00Introduction
2. 0:28Yet Analytics
3. 2:23Yet Visualization
4. 2:39Background
5. 3:23Objectrelational impedance mismatch
6. 3:55materialized views of data
7. 4:07choice of language
8. 4:28community
9. 4:52pivot
10. 5:27Datomic API
11. 6:49Competition
12. 7:39MVP
13. 9:13Making a product
14. 10:21We were sad
15. 10:50Datomic
16. 11:20Event Sourcing
17. 12:16Datomic Lessons
18. 12:44Hackathon
19. 13:02Lookingglass
20. 13:37Events Sourcing
21. 14:05Our Response
22. 14:15Datascript
23. 15:27Rules
24. 16:22Rules for checkers
25. 17:05Conclusion
26. 17:48Hackathon Recap
27. 18:29Reframe
28. 20:09Datomic is not free
29. 21:00A couple years later
30. 21:52ETL
31. 22:24Football
32. 23:21Challenges
33. 24:03Artisanal Data
34. 24:28Sciency Problems
35. 25:00Web Scraping
36. 25:43Data Model
37. 26:20Extract in ETL
38. 26:49Transform in ETL
39. 27:41Datomic transactions
40. 28:31Datomic querying
41. 29:30Results of study
42. 30:13Tools
43. 30:53Data Science
44. 31:51Big Data
45. 33:30Data Architecture
46. 33:41DeTomic
47. 34:35Governance
48. 35:06Day Tomic
49. 35:17Whats Next
50. 37:16Kafka Updates
51. 38:28Pluggable State Stores
52. 39:03De Tomic
53. 40:41Jason Lewis

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