Automate data pipeline orchestration using Apache Airflow

## 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

- [x] I would be interested in implementing this feature.

No se nombran archivos, pruebas ni puntos de entrada específicos. Empieza por localizar los scripts existentes de fetch, process y report para cada fuente de datos y, a continuación, determina cómo podrían asignarse a tareas de Airflow DAG; se considerará terminado cuando los workflows estén programados, sean observables y admitan el manejo de errores sin alterar las fases existentes.

Escrito por el modelo de indexación a partir del texto del issue.