Business Friendly Metadata bridges the gap between data engineering and business understanding. We explore how metadata, lineage, data contracts, mappings, and models can be explained, governed, and applied in a way that makes sense — to both business and technical audiences.
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Stop Losing Meaning in Your Data: Combating Semantic Drift With a Metadata-Driven, Text-First Architecture
How our YAML + DuckDB + draw.io + VSCode workflow keeps business logic trustworthy as the organization evolves
Summary
When product rules, categories, and KPIs evolve but data logic doesn’t, semantic drift creeps in — dashboards disagree, analysts burn hours reconciling numbers, and trust in analytics erodes. Building on AstroBee’s excellent primer on semantic drift, this article shows how our metadata-driven, text-first methodology — using YAML definitions, DuckDB for metadata, automated draw.io diagrams in VSCode, and Confluence publishing — builds institutional memory and keeps semantics aligned with business reality.
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Key insights
Semantic drift is inevitable if metric logic lives only in SQL or dashboards.
Our stack treats semantics as first-class code: YAML + version control + DuckDB store every change.
Automated diagram generation (draw.io + VSCode) keeps models and mappings visually aligned with metadata.
Drift detection & review workflow catch new statuses, schema changes, and business rule shifts early.
Publishing to Confluence turns silent tribal knowledge into…
Business Friendly Metadata bridges the gap between data engineering and business understanding. We explore how metadata, lineage, data contracts, mappings, and models can be explained, governed, and applied in a way that makes sense — to both business and technical audiences.
