Problem

Each data source in the project currently follows a similar workflow that involves fetching, processing, and report generation. However, these steps are executed through individual scripts, which can make the process difficult to automate, monitor, and maintain — especially when scaling to multiple data sources.

Description

I propose using Apache Airflow to orchestrate and automate the data workflows.
Each data source can be represented as an Airflow DAG, with tasks corresponding to each stage:

• Fetch: Collect data from APIs or external sources.

• Process: Clean, transform, and aggregate the data.

• Report: Generate summaries or metrics for analysis.

This approach would improve visibility, scheduling, error handling, and reusability across all data pipelines.

Alternatives

• Continue using manual script execution

• Implement lightweight scheduling within Python scripts.

Additional context

This integration aligns with the existing three-phase workflow structure and can easily wrap around current scripts without major codebase refactoring. Airflow also supports modular task development, which would benefit future contributors.

Implementation

• I would be interested in implementing this feature.