Data Platforms Transformation - Why and How
summary
Platform transformation is required when patchwork stops working and scale, latency or change management break; expect a minimum three‑year program with a hero project delivered in year one and most analytics refactored by year two. Inventory the stack (producers, persistent store, consumers), flag green/yellow/red components, and plan a decommissioning plan before introducing replacements. Account for rapid vendor churn (Snowflake pushed many releases in months), build cross‑functional teams, train end users, and iterate small, high‑impact deliveries.
AI-written summary · about
chapters (8)
- 0:00Intro
- 4:00Horizontals
- 8:45Stress Points
- 13:40Technology
- 14:20Data maturity
- 15:25The plan
- 18:20Working with people
- 22:20External factors
related talks
52mScaling Data Teams: Three Pitfalls and How To Avoid Them
47mHow to pick the best Data transformation strategy
41mModern Data Stack: Optimising and Scaling Data in a Tech Company
26mData & Analytics Strategy: 5 Overlooked Components
58mTales of Data Architecture Evolution
46mModernizing DataPlatform & Accelerating Business With Insights
25mPlatform Engineering for Modern Data Infrastructure
50mMasterclass: Building a data stack in 2021