A data platform’s structure and transformation logic are data, not code. Entities, mappings, filters, time policies, rules, lineage — and even the generated SQL — live as rows in one metamodel. 268 tables. 16 subject areas. Every question about the platform becomes a SQL query instead of an archaeology project.
MDDE’s bet: a data platform’s structure and transformation logic are data. Entities, attributes, mappings, filters, time policies, rules and the generated SQL live as rows in a metamodel — and the platform artifacts (DDL, dbt, pipelines, documentation, diagrams) are generated views of that metadata.
The 268 tables are the price of making every question — lineage, impact, quality, history, who-decided-what — a query instead of guesswork. This model exists to show that each table earns its price. It even documents itself: the reference docs, DDL and diagrams are generated from the metamodel database. A metadata framework whose own documentation is hand-maintained would be a contradiction.
Four hubs everything hangs off: model → layer → entity → attribute. From there: mappings (the SELECT as data), filters, time, rules, lineage, governance and knowledge.
Every table assigned to exactly one area; 268 base tables classified core / optional, hub / sub / satellite.
The irreducible kernel: model → layer → entity → attribute, plus keys, identifiers, inheritance, domains and subject areas as first-class citizens.
A SQL query as a structured object: FROM/JOIN, SELECT list, WHERE, GROUP BY, windows, functions and filters — each clause its own table. Impact analysis becomes a query.
Time reified, not hidden in SQL: reusable time profiles, point-in-time generation, timeline consolidation, materialized temporal filters.
MDM as data: contributors with trust scores, key harmonization, probabilistic matching, golden records with survivorship audit — which value won and why.
Ingest an existing Data Vault and reason about it — or migrate away: detected hubs/links/satellites, plan-execute-verify migration, effectivity and multi-active patterns.
BEAM modelstorming and FCO-IM fact-oriented modeling, implemented as data — with requirements-to-implementation traceability a document can never give you.
Reconstruct a model from legacy SQL: mined keys and relationships with confidence and review, lineage, complexity, safe-restructuring proposals with regression tests.
Rules as data (AnaCredit-grade), contracts, gates, tests, incidents, anomalies and a quality score with a time axis. The area that’s seen the most real use.
Prove new equals old: source-vs-target and query-pair comparison with per-column tolerance and row-level evidence — the trail auditors want.
Everything the engine does, recorded as data: runs, SQL generation and patches, table/column profiling, load history, freshness, metrics and alerts.
Metadata change governed like data change: per-object version snapshots, field-level audit, environments, releases and git-style merge for metadata.
The model generates its own pipelines: DAG, tasks, dependencies and schedules — plus delivery expectations for ‘delivery is late’ alerting.
The people-and-meaning layer: glossary, synonyms, ownership, tags, data mesh, access control, verified query-to-SQL pairs — the seed of a semantic layer.
A lightweight tracker embedded in the metamodel, so work links to the exact entity it concerns instead of a disconnected ticket system.
Bridges to DBML, PowerDesigner, Unity Catalog and Power BI — down to individual visuals, so BI lineage reaches into the report.
Diagrams are views of the model, never the model itself: definitions, placed symbols, reusable styles and user templates with a render history.
Totals: 16 areas · 268 base tables (+4 views) · every table placed on column evidence.
The thinking behind the model — structure, metadata organization, and why a solid model is what AI actually needs.
Before tags, before search: the organization of metadata. Structure, maps of content, and sidecars — so both you and the AI can find your way. (in progress)
A model isn’t a diagram of tables. It’s business knowledge, made explicit and queryable. (draft)
AI doesn’t need more prose. It needs a solid, readable model. Build the structure, and the AI can use it. (draft)
The enterprise logical data model, not as a wall poster — as a metamodel that generates real artifacts. (draft)
Articles publish on Structure Beats Magic and Medium. This section links the metadata pieces as they go live.
This model is the substance under Breakthrough Modeling — the new way of modeling that unites structure, movement, meaning and AI in one model. MDDE (model-driven data engineering) is the generation side; this metamodel is what it generates from.