Python Data Analytics Pipeline

Python-based data pipeline transforming transactional backend data into analytical models and business metrics.

Overview

This project bridges backend transactional data with analytics needs, focusing on clarity, reproducibility, and business relevance.

Data Flow

1. Extract raw transactional data
2. Transform and normalize records
3. Load analytical models
4. Compute business metrics

Metrics Examples

• Monthly Recurring Revenue (MRR)
• Customer churn rate
• Average revenue per user (ARPU)
• Lifetime value (LTV)

Tech Stack

• Python
• PostgreSQL
• SQL
• Pandas
• Airflow or Prefect

Design Decisions

• Separate raw and analytical schemas
• Prefer SQL for transformations where appropriate
• Python for orchestration and complex logic

Assumptions

• Data volume fits within a single database
• Batch processing is sufficient

Trade-offs

• No real-time analytics
• No distributed processing frameworks

Non-Goals

• Big data tooling
• Machine learning models

Possible Extensions

• Incremental loads
• Data quality checks