Data Mesh in the wild
summary
Slides claim 50–80% of data team time goes to finding and prepping data, and propose measurable outcomes: reduce time‑to‑insight, cut data downtime incidents, and increase reuse. Concrete builds include a simple monitoring service that runs SQL on a schedule and posts alerts to Slack, a feature store for feature reuse, and lightweight data contracts (Google docs or git). Case examples cite HelloFresh foundational models and report faster development and fewer incidents.
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chapters (7)
- 0:00Introduction
- 3:55Why data mesh
- 12:17What is data mesh
- 18:15Measuring data mesh success
- 29:31Summary
- 30:56Q&A: Organizational fit
- 35:26Q&A: Prioritization
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