See the gold layer.
Raw source data is refined into clean, business-ready context by a dbt medallion pipeline. Aurelia’s applications, semantic tools and AI clients build on the top layer — gold.
Bronze → silver → gold
After a source lands (see Connect your sources), dbt models refine it through three layers, each building on the one before:
| Layer | What it holds | Purpose |
|---|---|---|
| Bronze | Raw source data, as landed | A faithful copy of what each connector replicated — nothing dropped. |
| Silver | Typed, conformed, de-duplicated | Consistent types, keys and grain across sources; duplicates and noise removed. |
| Gold | Decision-ready marts | Governed business context shaped for applications, tools and agents. |
The gold marts
The gold marts live in the workspace’s isolated BigQuery gold dataset. Each one is reusable infrastructure: workspace views inspect it, while Aurelia Auto and MCP clients access it through the governed semantic layer.
| Mart | Contents |
|---|---|
aurelia_gold_<tenant>.daily | Daily channel performance — spend, reported revenue, orders and new customers. |
aurelia_gold_<tenant>.sales | Sales by region and category — revenue, contribution margin and orders. |
aurelia_gold_<tenant>.channel | Channel incrementality — MMM incremental revenue and each channel’s modelled share. |
aurelia_gold_<tenant>.cohort | Acquisition cohorts and predicted lifetime value. |
aurelia_gold_<tenant>.dim_meta | Dataset metadata — date bounds and filter members. Not governed. |
Who reads gold
These applications and agents build on the gold layer, so every interface works from the same governed context:
- Profitability — revenue, contribution and incrementality.
- Customer LTV — acquisition economics, value and payback.
- Total commerce — owned and marketplace demand in one view.
- Customer 360 — unified identities, lifecycle segments and source coverage.
- Aurelia Auto — reads the same channel-performance marts through the semantic layer to explain results and draft budget moves.
How it’s built & isolated
In production, Airbyte lands raw data and dbt builds each layer in separate tenant-isolated BigQuery datasets. Dataset names are derived on the server from the authenticated workspace; clients cannot choose another tenant’s dataset.