mirror of
https://github.com/datahub-project/datahub.git
synced 2025-07-05 08:07:04 +00:00
21 lines
1.8 KiB
Markdown
21 lines
1.8 KiB
Markdown
Ingesting metadata from dbt requires either using the **dbt** module or the **dbt-cloud** module.
|
|
|
|
### Concept Mapping
|
|
|
|
| Source Concept | DataHub Concept | Notes |
|
|
| -------------- | ---------------------------------------------------------------------- | ------------------ |
|
|
| Source | [Dataset](../../metamodel/entities/dataset.md) | Subtype `Source` |
|
|
| Seed | [Dataset](../../metamodel/entities/dataset.md) | Subtype `Seed` |
|
|
| Model | [Dataset](../../metamodel/entities/dataset.md) | Subtype `Model` |
|
|
| Snapshot | [Dataset](../../metamodel/entities/dataset.md) | Subtype `Snapshot` |
|
|
| Test | [Assertion](../../metamodel/entities/assertion.md) | |
|
|
| Test Result | [Assertion Run Result](../../metamodel/entities/assertion.md) | |
|
|
| Model Runs | [DataProcessInstance](../../metamodel/entities/dataProcessInstance.md) | |
|
|
|
|
Note:
|
|
|
|
1. You must **run ingestion for both dbt and your data warehouse** (target platform). They can be run in any order.
|
|
2. It generates column lineage between the `dbt` nodes (e.g. when a model/snapshot depends on a dbt source or ephemeral model) as well as lineage between the `dbt` nodes and the underlying target platform nodes (e.g. BigQuery Table -> dbt source, dbt model -> BigQuery table/view).
|
|
3. It automatically generates "sibling" relationships between the dbt nodes and the target / data warehouse nodes. These nodes will show up in the UI with both platform logos.
|
|
4. We also support automated actions (like add a tag, term or owner) based on properties defined in dbt meta.
|