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.

特定のファイル、テスト、またはエントリポイントは指定されていません。まず各データソースの既存の fetch、process、report スクリプトを見つけ、次にそれらを Airflow DAG タスクにどのように対応付けられるかを判断してください。ワークフローがスケジュールされ、可観測であり、既存のフェーズを中断することなくエラーハンドリングをサポートすれば完了です。

索引モデルが issue の本文から書いたものです。