Data Mesh testing: An opinionated view of what good looks like
summary
A practical 15-minute roadmap that prescribes specific tests per product type: unit and component tests for transformation logic, data quality checks and profiling (recommendation: run them in production if only one environment is possible), anomaly detection for historical drift (example: a ~200% overnight sales spike), schema contract tests to avoid breaking consumers, plus exploratory, flow, performance, security and federated computational governance checks to enforce cross-product SLAs and conformance.
AI-written summary · about
chapters (9)
- 0:00Intro
- 1:42Types of data products
- 4:35Single data product
- 11:18Preproduction environment
- 14:07Sourceline
- 15:18Data products
- 17:51Crossfunctional concerns
- 18:39Federated computational governance
- 19:23Summary
related talks
51mData Mesh – Successful with People, Process, and Technology
31mData Mesh In Practice – How To Set Up A Data Driven Organization
43mWhen design meets data: Using Data Mesh for a consumer and business-driven data strategy
30mData Mesh – Architecture And Insights From The Self-service Platform Boosting Financial Services
23mHow To “Data Mesh”: What You Need To Know To Make It Real
17mData Mesh: The Speedrun
44mData Mesh
34mBest and Worst Days of Data Mesh