Enterprise Operations Data Platform
Relational data platform with normalised schema for enterprise operations, featuring ETL pipelines and analytics-ready datasets.
Tech Stack
About
A production-ready PostgreSQL-based data platform that processes operational business data into analytics-ready datasets. This project exemplifies enterprise-grade data engineering practices through a modular Python ETL implementation following industry best practices. The platform manages 10 operational tables capturing comprehensive transactional business activity including customers, orders, inventory, payments, shipments, and returns. It demonstrates a complete data warehouse architecture suitable for business intelligence and reporting, with data quality validation at each stage.
Technical Approach
Implemented using a layered architecture with three distinct stages: Extract (from source systems), Transform (using Python/Pandas), and Load (into analytics tables). The platform uses DAG-based orchestration mimicking production frameworks like Apache Airflow. The technical implementation includes: - Modular Python ETL components for each data domain - PostgreSQL for both transactional and analytical storage - Data validation checks including NULL detection and completeness validation - Pandas-based transformation logic for complex business rules - Analytics-ready output tables optimized for BI tools (Power BI/Tableau)
Key Achievements
- ✓Designed and implemented 10 normalized operational tables handling thousands of transactional records
- ✓Created modular ETL pipeline processing 9,000+ order items and 1,000+ customer records
- ✓3 analytics tables generated ready for business intelligence and reporting
- ✓Implemented data quality validation checks preventing bad data from downstream use
- ✓Demonstrated complete data warehouse architecture from source to analytics
- ✓Optimized schema design for analytical query performance
Challenges & Learning
- •Designing normalized schemas that balance transactional integrity with analytical performance
- •Implementing robust data validation to detect and handle missing or corrupt data
- •Managing incremental data loads without reprocessing historical data
- •Optimizing SQL queries for complex analytical requirements
- •Handling diverse data types and business rules across multiple domains
- •Documentation and maintainability of complex ETL logic