Tech Tales: How Data DX enables business success
summary
Two startups build a cat social app with Postgres, Express and React, then diverge: one uses plain SQL and hand‑rolled Redis caching and Kafka messaging, the other adopts Prisma ORM, Prisma Accelerate for global caching and connection pooling, and a DB change stream service (Prisma P). The SQL team hits failed migrations, lost developer time, cache invalidation bugs, message loss and exhausted DB connections; the Prisma stack ships features faster, scales globally and keeps latency and costs down.
AI-written summary · about
chapters (6)
- 0:00Introduction to Data DX
- 1:24Two startups and their MVP
- 4:58Adding a caching layer
- 8:29Real-time features for users
- 13:29Moving to the edge
- 17:23Key takeaways and conclusion
related talks
24mDoing Data as a Frontend Developer
50mMasterclass: Building a data stack in 2021
52mScaling Data Teams: Three Pitfalls and How To Avoid Them
36mApps, stacks & frameworks: Avoiding "Shiny Object" Syndrome
07mInterview
30mData Rainbows - select * from cloud;
33mStrategic Node.js crossroads in 2023
24mApps, Stacks, and Frameworks