dbt Integration

Install the package

Add the MetricRegistry package to your packages.yml and run dbt deps.

packages:
  - package: metricregistry/metricregistry_uk
    version: ">=0.1.0"

dbt deps

Configure dataset mappings

Map each of the 18 registry datasets to your warehouse tables or dbt models using the metricregistry_datasets variable in dbt_project.yml.

vars:
  metricregistry_datasets:
    capital:      "{{ ref('stg_capital') }}"
    exposures:    "{{ ref('stg_exposures') }}"
    credit:       "{{ source('banking', 'credit_data') }}"
    liquidity:    "{{ ref('stg_liquidity') }}"
    # ... map each dataset to your warehouse table

The 18 available datasets are:

Run metrics

Each metric materializes as a view in your warehouse. Use standard dbt selectors to run all metrics or a targeted subset.

# Run all 299 metrics
dbt run --select metricregistry_uk

# Run only banking metrics
dbt run --select metricregistry_uk.banking

# Run a specific metric
dbt run --select metricregistry_uk.banking.capital.cet1_ratio

MetricFlow export

Fetch dbt 1.6+ compatible MetricFlow YAML for any metric via the REST API. The response includes semantic_models and metrics blocks ready to drop into your dbt project.

curl https://api.metricregistry.co.uk/metrics/uk.banking.capital.cet1_ratio/export/metricflow \
  -H "Authorization: Bearer mrk_..."

How it works

The package uses a small set of macros to translate registry metric definitions into runnable SQL at compile time:

• The mr_dataset() macro resolves dataset names to your configured table references from metricregistry_datasets.
• Template variables in SQL (${column_name}) become actual column references resolved at compile time.
• Each metric materializes as a view by default. Override with standard dbt config to use table or incremental.
• Models are tagged with metricregistry, the industry, and the category for easy selection and documentation.