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https://github.com/databricks/dbt-databricks
A dbt adapter for Databricks.
https://github.com/databricks/dbt-databricks
databricks dbt etl sql
Last synced: about 4 hours ago
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A dbt adapter for Databricks.
- Host: GitHub
- URL: https://github.com/databricks/dbt-databricks
- Owner: databricks
- License: apache-2.0
- Created: 2021-10-19T16:26:44.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-29T17:28:21.000Z (15 days ago)
- Last Synced: 2024-10-29T20:23:47.979Z (14 days ago)
- Topics: databricks, dbt, etl, sql
- Language: Python
- Homepage: https://databricks.com
- Size: 3.96 MB
- Stars: 219
- Watchers: 22
- Forks: 120
- Open Issues: 80
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.MD
- License: License.md
- Codeowners: .github/CODEOWNERS
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README
**[dbt](https://www.getdbt.com/)** enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
The **[Databricks Lakehouse](https://www.databricks.com/)** provides one simple platform to unify all your data, analytics and AI workloads.
# dbt-databricks
The `dbt-databricks` adapter contains all of the code enabling dbt to work with Databricks. This adapter is based off the amazing work done in [dbt-spark](https://github.com/dbt-labs/dbt-spark). Some key features include:
- **Easy setup**. No need to install an ODBC driver as the adapter uses pure Python APIs.
- **Open by default**. For example, it uses the the open and performant [Delta](https://delta.io/) table format by default. This has many benefits, including letting you use `MERGE` as the the default incremental materialization strategy.
- **Support for Unity Catalog**. dbt-databricks>=1.1.1 supports the 3-level namespace of Unity Catalog (catalog / schema / relations) so you can organize and secure your data the way you like.
- **Performance**. The adapter generates SQL expressions that are automatically accelerated by the native, vectorized [Photon](https://databricks.com/product/photon) execution engine.
## Choosing between dbt-databricks and dbt-spark
If you are developing a dbt project on Databricks, we recommend using `dbt-databricks` for the reasons noted above.
`dbt-spark` is an actively developed adapter which works with Databricks as well as Apache Spark anywhere it is hosted e.g. on AWS EMR.
## Getting started
### Installation
Install using pip:
```nofmt
pip install dbt-databricks
```
Upgrade to the latest version
```nofmt
pip install --upgrade dbt-databricks
```
### Profile Setup
```nofmt
your_profile_name:
target: dev
outputs:
dev:
type: databricks
catalog: [optional catalog name, if you are using Unity Catalog, only available in dbt-databricks>=1.1.1]
schema: [database/schema name]
host: [your.databrickshost.com]
http_path: [/sql/your/http/path]
token: [dapiXXXXXXXXXXXXXXXXXXXXXXX]
```
### Quick Starts
These following quick starts will get you up and running with the `dbt-databricks` adapter:
- [Developing your first dbt project](https://github.com/databricks/dbt-databricks/blob/main/docs/local-dev.md)
- Using dbt Cloud with Databricks ([Azure](https://docs.microsoft.com/en-us/azure/databricks/integrations/prep/dbt-cloud) | [AWS](https://docs.databricks.com/integrations/prep/dbt-cloud.html))
- [Running dbt production jobs on Databricks Workflows](https://github.com/databricks/dbt-databricks/blob/main/docs/databricks-workflows.md)
- [Using Unity Catalog with dbt-databricks](https://github.com/databricks/dbt-databricks/blob/main/docs/uc.md)
- [Using GitHub Actions for dbt CI/CD on Databricks](https://github.com/databricks/dbt-databricks/blob/main/docs/github-actions.md)
- [Loading data from S3 into Delta using the databricks_copy_into macro](https://github.com/databricks/dbt-databricks/blob/main/docs/databricks-copy-into-macro-aws.md)
- [Contribute to this repository](CONTRIBUTING.MD)
### Compatibility
The `dbt-databricks` adapter has been tested:
- with Python 3.7 or above.
- against `Databricks SQL` and `Databricks runtime releases 9.1 LTS` and later.
### Tips and Tricks
## Choosing compute for a Python model
You can override the compute used for a specific Python model by setting the `http_path` property in model configuration. This can be useful if, for example, you want to run a Python model on an All Purpose cluster, while running SQL models on a SQL Warehouse. Note that this capability is only available for Python models.
```
def model(dbt, session):
dbt.config(
http_path="sql/protocolv1/..."
)
```