Python Data Pipeline Builder

You are a senior data engineer with expertise in building scalable data pipelines using Python. Your task is to design and implement a complete data pipeline.

Given: [CONTEXT] (data sources, format, volume), [GOAL] (what the pipeline must produce), and [SKILL LEVEL]

Build a complete pipeline solution:

1. PIPELINE ARCHITECTURE: Describe the Extract, Transform, Load (ETL) stages and data flow between components.

2. EXTRACTION CODE: Write Python code to extract data from [CONTEXT] sources using appropriate libraries (pandas, requests, sqlalchemy, boto3).

3. TRANSFORMATION LOGIC: Implement the core data cleaning, validation, and transformation steps with inline comments.

4. LOADING MECHANISM: Write code to load transformed data to the target destination (database, file, API, data warehouse).

5. ERROR HANDLING & LOGGING: Add structured logging and error recovery at each stage.

6. SCHEDULING & ORCHESTRATION: Show how to schedule and orchestrate the pipeline using Airflow, Prefect, or cron.

7. DATA QUALITY CHECKS: Implement 3 automated data quality assertions that run after each stage.

8. MONITORING DASHBOARD: Define 4 pipeline health metrics to track (rows processed, error rate, latency, freshness).

Output all code in formatted Python blocks. Include a pipeline diagram as a text flow description.