README

Overview

dbt is a data platform used to store and query analytical or operational data. Learn more in the official dbt documentation.

The DataHub integration for dbt covers core metadata entities such as datasets/tables/views, schema fields, and containers. Depending on module capabilities, it can also capture features such as lineage, usage, profiling, ownership, tags, and stateful deletion detection.

:::info Run both dbt and data warehouse ingestion for lineage

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. :::

Concept Mapping

Source Concept	DataHub Concept	Notes
Source	Dataset	Subtype Source
Seed	Dataset	Subtype Seed
Model	Dataset	Subtype Model
Snapshot	Dataset	Subtype Snapshot
Semantic View	Dataset	Subtype Semantic View
Test	Assertion
Test Result	Assertion Run Result
Model Runs	DataProcessInstance