https://github.com/gocardless/airflow-dbt
Apache Airflow integration for dbt
https://github.com/gocardless/airflow-dbt
airflow airflow-dbt dbt
Last synced: 4 months ago
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Apache Airflow integration for dbt
- Host: GitHub
- URL: https://github.com/gocardless/airflow-dbt
- Owner: gocardless
- License: mit
- Archived: true
- Created: 2020-02-24T16:54:33.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-17T06:46:00.000Z (11 months ago)
- Last Synced: 2024-11-28T02:36:12.898Z (5 months ago)
- Topics: airflow, airflow-dbt, dbt
- Language: Python
- Homepage: https://pypi.org/project/airflow-dbt/
- Size: 66.4 KB
- Stars: 397
- Watchers: 62
- Forks: 61
- Open Issues: 32
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
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- jimsghstars - gocardless/airflow-dbt - Apache Airflow integration for dbt (Python)
README
# airflow-dbt
This is a collection of [Airflow](https://airflow.apache.org/) operators to provide easy integration with [dbt](https://www.getdbt.com).
```py
from airflow import DAG
from airflow_dbt.operators.dbt_operator import (
DbtSeedOperator,
DbtSnapshotOperator,
DbtRunOperator,
DbtTestOperator,
DbtCleanOperator,
)
from airflow.utils.dates import days_ago
default_args = {
'dir': '/srv/app/dbt',
'start_date': days_ago(0)
}
with DAG(dag_id='dbt', default_args=default_args, schedule_interval='@daily') as dag:
dbt_seed = DbtSeedOperator(
task_id='dbt_seed',
)
dbt_snapshot = DbtSnapshotOperator(
task_id='dbt_snapshot',
)
dbt_run = DbtRunOperator(
task_id='dbt_run',
)
dbt_test = DbtTestOperator(
task_id='dbt_test',
retries=0, # Failing tests would fail the task, and we don't want Airflow to try again
)
dbt_clean = DbtCleanOperator(
task_id='dbt_clean',
)
dbt_seed >> dbt_snapshot >> dbt_run >> dbt_test >> dbt_clean
```
## Installation
Install from PyPI:
```sh
pip install airflow-dbt
```
It will also need access to the `dbt` CLI, which should either be on your `PATH` or can be set with the `dbt_bin` argument in each operator.
## Usage
There are five operators currently implemented:
* `DbtDocsGenerateOperator`
* Calls [`dbt docs generate`](https://docs.getdbt.com/reference/commands/cmd-docs)
* `DbtDepsOperator`
* Calls [`dbt deps`](https://docs.getdbt.com/docs/deps)
* `DbtSeedOperator`
* Calls [`dbt seed`](https://docs.getdbt.com/docs/seed)
* `DbtSnapshotOperator`
* Calls [`dbt snapshot`](https://docs.getdbt.com/docs/snapshot)
* `DbtRunOperator`
* Calls [`dbt run`](https://docs.getdbt.com/docs/run)
* `DbtTestOperator`
* Calls [`dbt test`](https://docs.getdbt.com/docs/test)
* `DbtCleanOperator`
* Calls [`dbt clean`](https://docs.getdbt.com/docs/clean)
Each of the above operators accept the following arguments:
* `env`
* If set as a kwarg dict, passed the given environment variables as the arguments to the dbt task
* `profiles_dir`
* If set, passed as the `--profiles-dir` argument to the `dbt` command
* `target`
* If set, passed as the `--target` argument to the `dbt` command
* `dir`
* The directory to run the `dbt` command in
* `full_refresh`
* If set to `True`, passes `--full-refresh`
* `vars`
* If set, passed as the `--vars` argument to the `dbt` command. Should be set as a Python dictionary, as will be passed to the `dbt` command as YAML
* `models`
* If set, passed as the `--models` argument to the `dbt` command
* `exclude`
* If set, passed as the `--exclude` argument to the `dbt` command
* `select`
* If set, passed as the `--select` argument to the `dbt` command
* `selector`
* If set, passed as the `--selector` argument to the `dbt` command
* `dbt_bin`
* The `dbt` CLI. Defaults to `dbt`, so assumes it's on your `PATH`
* `verbose`
* The operator will log verbosely to the Airflow logs
* `warn_error`
* If set to `True`, passes `--warn-error` argument to `dbt` command and will treat warnings as errors
Typically you will want to use the `DbtRunOperator`, followed by the `DbtTestOperator`, as shown earlier.
You can also use the hook directly. Typically this can be used for when you need to combine the `dbt` command with another task in the same operators, for example running `dbt docs` and uploading the docs to somewhere they can be served from.
## Building Locally
To install from the repository:
First it's recommended to create a virtual environment:
```bash
python3 -m venv .venv
source .venv/bin/activate
```
Install using `pip`:
```bash
pip install .
```
## Testing
To run tests locally, first create a virtual environment (see [Building Locally](https://github.com/gocardless/airflow-dbt#building-locally) section)
Install dependencies:
```bash
pip install . pytest
```
Run the tests:
```bash
pytest tests/
```
## Code style
This project uses [flake8](https://flake8.pycqa.org/en/latest/).
To check your code, first create a virtual environment (see [Building Locally](https://github.com/gocardless/airflow-dbt#building-locally) section):
```bash
pip install flake8
flake8 airflow_dbt/ tests/ setup.py
```
## Package management
If you use dbt's package manager you should include all dependencies before deploying your dbt project.
For Docker users, packages specified in `packages.yml` should be included as part your docker image by calling `dbt deps` in your `Dockerfile`.
## Amazon Managed Workflows for Apache Airflow (MWAA)
If you use MWAA, you just need to update the `requirements.txt` file and add `airflow-dbt` and `dbt` to it.
Then you can have your dbt code inside a folder `{DBT_FOLDER}` in the dags folder on S3 and configure the dbt task like below:
```python
dbt_run = DbtRunOperator(
task_id='dbt_run',
dbt_bin='/usr/local/airflow/.local/bin/dbt',
profiles_dir='/usr/local/airflow/dags/{DBT_FOLDER}/',
dir='/usr/local/airflow/dags/{DBT_FOLDER}/'
)
```
## Templating and parsing environments variables
If you would like to run DBT using custom profile definition template with environment-specific variables, like for example profiles.yml using jinja:
```yaml
:
outputs:
:
database: "{{ env_var('DBT_ENV_SECRET_DATABASE') }}"
password: "{{ env_var('DBT_ENV_SECRET_PASSWORD') }}"
schema: "{{ env_var('DBT_ENV_SECRET_SCHEMA') }}"
threads: "{{ env_var('DBT_THREADS') }}"
type:
user: "{{ env_var('USER_NAME') }}_{{ env_var('ENV_NAME') }}"
target:
```
You can pass the environment variables via the `env` kwarg parameter:
```python
import os
...
dbt_run = DbtRunOperator(
task_id='dbt_run',
env={
'DBT_ENV_SECRET_DATABASE': '',
'DBT_ENV_SECRET_PASSWORD': '',
'DBT_ENV_SECRET_SCHEMA': '',
'USER_NAME': '',
'DBT_THREADS': os.getenv(''),
'ENV_NAME': os.getenv('ENV_NAME')
}
)
```
## License & Contributing
* This is available as open source under the terms of the [MIT License](http://opensource.org/licenses/MIT).
* Bug reports and pull requests are welcome on GitHub at https://github.com/gocardless/airflow-dbt.
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