Modern Data Stack: New Terminology Made Clear
summary
Defines four concrete solutions and where they fit: data observability (freshness, distribution, volume, schema, lineage) aims to stop data downtime and automate alerts across pipelines and jobs, with vendors like Monte Carlo inspiring the framing; active metadata is an always-on, API-enabled catalog that triggers actions (masking, purge, scale) and adds operational, business and social context; reverse ETL pushes segments back into marketing or finance tools rather than only into the warehouse; metrics layer (DBT beta) centralizes business metric logic so any BI tool or API returns the same count.
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chapters (6)
- 0:00Introduction and overview
- 4:56Data observability explained
- 12:37Active metadata concept
- 18:52Reverse ETL purpose
- 20:55Metrics layer importance
- 27:32Summary and key takeaways
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