<p align="center">
<img src="https://bynder-public-us-west-2.s3.amazonaws.com/styleguide/ABB317701CA31CB7F29268E32B303CAE-pdf-column-1.png" alt="databricks logo" width="50%" />
<img src="https://raw.githubusercontent.com/dbt-labs/dbt/ec7dee39f793aa4f7dd3dae37282cc87664813e4/etc/dbt-logo-full.svg" alt="dbt logo" width="250"/>
</p>
<p align="center">
<a href="https://github.com/databricks/dbt-databricks/actions/workflows/main.yml">
<img src="https://github.com/databricks/dbt-databricks/actions/workflows/main.yml/badge.svg?event=push" alt="Unit Tests Badge"/>
</a>
<a href="https://github.com/databricks/dbt-databricks/actions/workflows/integration.yml">
<img src="https://github.com/databricks/dbt-databricks/actions/workflows/integration.yml/badge.svg?event=push" alt="Integration Tests Badge"/>
</a>
</p>
**[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/..."
)
```
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"description": "<p align=\"center\">\n <img src=\"https://bynder-public-us-west-2.s3.amazonaws.com/styleguide/ABB317701CA31CB7F29268E32B303CAE-pdf-column-1.png\" alt=\"databricks logo\" width=\"50%\" />\n <img src=\"https://raw.githubusercontent.com/dbt-labs/dbt/ec7dee39f793aa4f7dd3dae37282cc87664813e4/etc/dbt-logo-full.svg\" alt=\"dbt logo\" width=\"250\"/>\n</p>\n<p align=\"center\">\n <a href=\"https://github.com/databricks/dbt-databricks/actions/workflows/main.yml\">\n <img src=\"https://github.com/databricks/dbt-databricks/actions/workflows/main.yml/badge.svg?event=push\" alt=\"Unit Tests Badge\"/>\n </a>\n <a href=\"https://github.com/databricks/dbt-databricks/actions/workflows/integration.yml\">\n <img src=\"https://github.com/databricks/dbt-databricks/actions/workflows/integration.yml/badge.svg?event=push\" alt=\"Integration Tests Badge\"/>\n </a>\n</p>\n\n**[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.\n\nThe **[Databricks Lakehouse](https://www.databricks.com/)** provides one simple platform to unify all your data, analytics and AI workloads.\n\n# dbt-databricks\n\nThe `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:\n\n- **Easy setup**. No need to install an ODBC driver as the adapter uses pure Python APIs.\n- **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.\n- **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.\n- **Performance**. The adapter generates SQL expressions that are automatically accelerated by the native, vectorized [Photon](https://databricks.com/product/photon) execution engine.\n\n## Choosing between dbt-databricks and dbt-spark\nIf you are developing a dbt project on Databricks, we recommend using `dbt-databricks` for the reasons noted above.\n\n`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.\n\n## Getting started\n\n### Installation\n\nInstall using pip:\n```nofmt\npip install dbt-databricks\n```\n\nUpgrade to the latest version\n```nofmt\npip install --upgrade dbt-databricks\n```\n\n### Profile Setup\n\n```nofmt\nyour_profile_name:\n target: dev\n outputs:\n dev:\n type: databricks\n catalog: [optional catalog name, if you are using Unity Catalog, only available in dbt-databricks>=1.1.1]\n schema: [database/schema name]\n host: [your.databrickshost.com]\n http_path: [/sql/your/http/path]\n token: [dapiXXXXXXXXXXXXXXXXXXXXXXX]\n```\n\n### Quick Starts\n\nThese following quick starts will get you up and running with the `dbt-databricks` adapter:\n- [Developing your first dbt project](https://github.com/databricks/dbt-databricks/blob/main/docs/local-dev.md)\n- 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))\n- [Running dbt production jobs on Databricks Workflows](https://github.com/databricks/dbt-databricks/blob/main/docs/databricks-workflows.md)\n- [Using Unity Catalog with dbt-databricks](https://github.com/databricks/dbt-databricks/blob/main/docs/uc.md)\n- [Using GitHub Actions for dbt CI/CD on Databricks](https://github.com/databricks/dbt-databricks/blob/main/docs/github-actions.md)\n- [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)\n- [Contribute to this repository](CONTRIBUTING.MD)\n\n### Compatibility\n\nThe `dbt-databricks` adapter has been tested:\n\n- with Python 3.7 or above.\n- against `Databricks SQL` and `Databricks runtime releases 9.1 LTS` and later.\n\n### Tips and Tricks\n## Choosing compute for a Python model\nYou 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.\n\n```\ndef model(dbt, session):\n dbt.config(\n http_path=\"sql/protocolv1/...\"\n )\n```\n",
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