> For the complete documentation index, see [llms.txt](https://docs-old.evidentlyai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-old.evidentlyai.com/v0.1.57/integrations/evidently-and-airflow.md).

# Evidently and Airflow

Apache Airflow is an open-source [workflow management tool](https://airflow.apache.org).

You can use this integration to generate JSON profiles or HTML reports and as a step in the Airflow DAG.

**An integration example is available as a Docker container:**

{% embed url="<https://github.com/evidentlyai/evidently/tree/main/examples/integrations/airflow_drift_detection>" %}

Follow the readme to install and modify the example.

It contains two specific DAGs that match common batch monitoring needs.

### 1. Generate model performance reports as a batch job

You can generate an Evidently report (e.g. a data drift report) every time the new data arrives. You can then store it in your file system.

**Here is a DAG example:**

{% embed url="<https://github.com/evidentlyai/evidently/blob/main/examples/integrations/airflow_drift_detection/dags/evidently_drift_dashboard.py>" %}

### 2. Generate visual performance reports on defined conditions

You might not always need to generate visual reports every time.

For example, you can run checks on the model performance and only generate the reports if a certain condition is satisfied. For example, if you detect drift or performance drop. Otherwise, you can simply log the results.

**Here is a DAG example:**

{% embed url="<https://github.com/evidentlyai/evidently/blob/main/examples/integrations/airflow_drift_detection/dags/evidently_conditional_drift_dashboard_generation.py>" %}

It works as the following:

* Run a data drift check by generating an Evidently JSON profile
* If the drift is not detected, log the JSON output
* If the drift is detected, generate and store a visual HTML report