> ## Documentation Index
> Fetch the complete documentation index at: https://docs.open-metadata.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Ingest Exposures from dbt | OpenMetadata Downstream Lineage Guide

> Ingest dbt exposures to extend lineage from your dbt models to the dashboards, ML models, and API endpoints that consume them.

# Ingest Exposures from dbt

Ingest dbt [exposures](https://docs.getdbt.com/docs/build/exposures) from the `manifest.json` file to extend lineage from your dbt models to the dashboards, ML models, and API endpoints that consume them.

## Requirements

<Tip>
  The downstream entity referenced by an exposure must already exist in OpenMetadata before the dbt ingestion runs. OpenMetadata matches it by Fully Qualified Name (FQN). If OpenMetadata can't find a match, it logs a warning and skips the exposure.
</Tip>

### Supported Exposure Types

OpenMetadata maps a dbt exposure to an OpenMetadata entity based on its `type` field:

| dbt Exposure `type` | OpenMetadata Entity |
| :------------------ | :------------------ |
| `dashboard`         | Dashboard           |
| `ml`                | ML Model            |
| `application`       | API Endpoint        |

Any other `type` value is skipped during ingestion.

## Ingesting dbt Exposures

Follow these steps to link a dbt exposure to an existing OpenMetadata entity.

1. **Add exposure information in your dbt project**

   Define the exposure in your dbt project (for example in `models/exposures.yml`), and set `meta.open_metadata_fqn` to the Fully Qualified Name of the corresponding entity already ingested in OpenMetadata.

   For more information, see [Add Exposures to your DAG](https://docs.getdbt.com/docs/build/exposures).

   ```yaml theme={null}
   exposures:
     - name: orders_dashboard
       label: orders
       type: dashboard
       maturity: high
       url: http://localhost:8080/looker/dashboard/8/
       description: >
         Orders dashboard built on top of the orders model.
       depends_on:
         - ref('fact_sales')
       meta:
         open_metadata_fqn: sample_looker.orders  # OpenMetadata entity Fully Qualified Name
   ```

   After adding the exposure, run your dbt workflow (`dbt compile` or `dbt run`). The generated `manifest.json` will include the exposure under the `exposures` key, with upstream models listed under `depends_on->nodes`.

   ```json theme={null}
   "exposure.jaffle_shop.orders_dashboard": {
     "name": "orders_dashboard",
     "resource_type": "exposure",
     "type": "dashboard",
     "meta": {
       "open_metadata_fqn": "sample_looker.orders"
     },
     "depends_on": {
       "nodes": [
         "model.jaffle_shop.fact_sales"
       ]
     }
   }
   ```

2. **Viewing the exposure lineage**

   Once ingested, the linked dashboard, ML model, or API endpoint appears as a downstream node in the **Lineage** tab of the upstream dbt table(s) listed under `depends_on`.

## Validation and Error Handling

| Scenario                                                           | Behavior                                 |
| ------------------------------------------------------------------ | ---------------------------------------- |
| Exposure `type` not one of `dashboard`, `ml`, `application`        | Warning logged, exposure skipped         |
| `meta.open_metadata_fqn` missing from exposure spec                | Warning logged, exposure skipped         |
| Entity referenced by `open_metadata_fqn` not found in OpenMetadata | Warning logged, exposure skipped         |
| Upstream model not yet ingested as an OpenMetadata table           | Upstream node excluded from lineage edge |