dbt Integration

dbt Integration

sup CLI can synchronize your dbt models with Superset datasets, keeping your analytics stack in sync.

Prerequisites

• dbt Core or dbt Cloud project
• Superset/Preset workspace with database connections

dbt Core Integration

Setup

Configure dbt paths:

sup config set dbt-project-dir /path/to/dbt/project
sup config set dbt-profiles-dir ~/.dbt

Sync Models to Datasets

# Sync all models
sup dataset sync-dbt

# Sync specific models
sup dataset sync-dbt --models staging.sales,marts.revenue

# With custom target
sup dataset sync-dbt --target production

dbt Cloud Integration

Setup

Configure dbt Cloud credentials:

sup config set dbt-cloud-account-id 12345
sup config set dbt-cloud-project-id 67890
sup config set dbt-cloud-job-id 11111
sup config set dbt-cloud-api-token "your-token"

Sync from dbt Cloud

# Sync from latest run
sup dataset sync-dbt-cloud

# Sync from specific run
sup dataset sync-dbt-cloud --run-id 123456

Mapping Configuration

Create a dbt_mapping.yml file:

models:
  staging.customers:
    superset_dataset: "STG_CUSTOMERS"
    metrics:
      - name: customer_count
        expression: COUNT(DISTINCT customer_id)
      - name: total_revenue
        expression: SUM(revenue)

  marts.sales:
    superset_dataset: "SALES_FACT"
    time_column: order_date
    dimensions:
      - customer_id
      - product_id
      - region

Exposures

Generate dbt exposures from Superset:

# Generate exposures YAML
sup chart export-exposures > models/exposures.yml

# Include in dbt project
sup chart export-exposures --output models/exposures/superset.yml

Best Practices

1. Use consistent naming between dbt models and Superset datasets
2. Document metrics in dbt and sync to Superset
3. Version control your mapping configurations
4. Test in staging before syncing to production
5. Use CI/CD to automate sync on dbt runs