Metadata-Version: 2.1
Name: airflow-provider-anomaly-detection
Version: 0.0.5
Summary: An airflow provider for anomaly detection.
Project-URL: Homepage, https://github.com/andrewm4894/airflow-provider-anomaly-detection
Project-URL: Bug Tracker, https://github.com/andrewm4894/airflow-provider-anomaly-detection/issues
Author-email: andrewm4894 <andrewm4894@gmail.com>
License-File: LICENSE
Classifier: Framework :: Apache Airflow
Classifier: Framework :: Apache Airflow :: Provider
Requires-Python: >=3.7
Description-Content-Type: text/markdown

# Anomaly Detection with Apache Airflow

Painless anomaly detection (using [PyOD](https://github.com/yzhao062/pyod)) with Apache Airflow.

1. Create and express your metrics via SQL queries.
1. Some YAML configuration fun.
1. Receive useful alerts when metrics look anomalous.

## Getting Started

Check out the [example dag](https://github.com/andrewm4894/airflow-provider-anomaly-detection/tree/main/airflow_anomaly_detection/example_dags/anomaly-detection-dag/) to get started.

### Installation

Install from [PyPI](https://pypi.org/project/airflow-provider-anomaly-detection/) as usual.

```bash
pip install airflow-provider-anomaly-detection
```

### Configuration

See the example configuration files in the [example dag](https://github.com/andrewm4894/airflow-provider-anomaly-detection/tree/main/airflow_anomaly_detection/example_dags/anomaly-detection-dag/config/) folder. You can use a `defaults.yaml` or specific `<metric-batch>.yaml` for each metric batch if needed.
