Import warehouse schemas

1. Identify your dbt schemas

You need the actual warehouse schema names. If you used the reference contoso project's schema convention, they're:

• contoso_dbt_raw - what dlt loads
• contoso_dbt_staging - stg_* models
• contoso_dbt_reports - rpt_* models

If you're not sure what your dbt project lands as, check dbt_project.yml. The full schema name is <target.schema>_<+schema> by default, or just <+schema> if you've overridden generate_schema_name (the contoso project does).

2. Run the import

From the dbt project root, point the import at your context/ pipeline folder. The --schemas flag is repeatable for BigQuery - pass it once per schema:

bruin import database \
  --config-file context/.bruin.yml \
  --connection contoso_dbt_bq \
  --schemas contoso_dbt_raw \
  --schemas contoso_dbt_staging \
  --schemas contoso_dbt_reports \
  context

For Postgres, Redshift, or ClickHouse, use --schema (singular) once per run:

bruin import database \
  --config-file context/.bruin.yml \
  --connection contoso_dbt_pg \
  --schema contoso_dbt_staging \
  context

Note: The trailing positional argument is the pipeline path, not the project root. That's context in our setup - the directory containing pipeline.yml.

3. Inspect what was generated

ls context/assets/

You should see one folder per schema, each containing one .asset.yml per table:

context/assets/
├── contoso_dbt_raw/
│   ├── customers.asset.yml
│   ├── orders.asset.yml
│   └── ...
├── contoso_dbt_staging/
│   ├── stg_customers.asset.yml
│   └── ...
└── contoso_dbt_reports/
    ├── rpt_revenue_by_segment.asset.yml
    └── ...

Open one - they look like this:

name: contoso_dbt_reports.rpt_revenue_by_segment
type: bq.source
columns:
  - name: segment_id
    type: STRING
  - name: category_name
    type: STRING
  - name: year
    type: INT64
  - name: revenue_usd
    type: NUMERIC

No descriptions, no tags, no checks yet - just structure. That's by design. The AI enhance step in the next chapter is what fills in the meaning.

4. Filter out loader-internal tables

If your raw schema was loaded by dlt, Fivetran, or Airbyte, the import will include their bookkeeping tables. They're not useful as agent context - drop them before enhancing so Claude doesn't waste time describing _dlt_pipeline_state:

# dlt
find context/assets -name "_dlt_*.asset.yml" -delete

# Airbyte
find context/assets -name "_airbyte_*.asset.yml" -delete

# Fivetran
find context/assets -name "fivetran_*.asset.yml" -delete

Run only the one(s) that match your loader. The contoso project uses dlt, so the first command applies.

5. (Optional) Validate the structure

Even before enhancement, you can sanity-check the YAMLs:

bruin validate --config-file context/.bruin.yml context

You should see one line per asset and a summary like:

✓ Successfully validated 40 assets across 1 pipeline, all good.

If you see parse errors, re-run the import for that schema - partial files can occasionally be left behind if the introspection is interrupted.