An end-to-end data engineering and analytics solution that converts operational business transactions into financial and operational decision intelligence.
This project models a real company Order-to-Cash (O2C) lifecycle and demonstrates how raw operational records become CFO-level reporting.
Companies collect transactional data from multiple operational systems:
- Orders system
- Shipping system
- Billing system
- Payments system
The problem: Data exists but leadership cannot answer critical questions:
• Why is cash collection delayed?
• Which customers create revenue vs risk?
• Which shipments impact invoice cycle time?
• Where is revenue leakage occurring?
This project builds a full analytics platform to solve that.
Raw Transactions → Data Lake → ETL Processing → SQL Warehouse → Data Model → BI Dashboard → Business Decisions
Operational CSV data is ingested into a cloud storage layer, validated and transformed through a Python ETL pipeline, structured into a dimensional warehouse model, and exposed to Power BI for analytics.
Contains unprocessed operational records:
- customers
- orders
- shipments
- invoices
- payments
This simulates ERP system exports.
Script: etl/otc_etl_with_validation.py
ETL performs:
- schema validation
- null handling
- date standardization
- duplicate removal
- business rule validation
- referential integrity checks
Structured relational warehouse built using fact and dimension design.
Fact Tables
- FactOrders
- FactInvoices
- FactPayments
- FactShipments
Dimension Tables
- DimCustomer
- DimDate
- DimProduct
Star schema enables BI performance and business readability.
Business-friendly views created:
- revenue view
- collection aging
- customer performance
- shipment delay metrics
Power BI connects to warehouse and produces KPI reporting dashboards.
Financial Performance
- Total Revenue
- Monthly Revenue Trend
- Outstanding Receivables
Operational Efficiency
- Order Fulfillment Cycle Time
- Shipment Delay Rate
- Invoice Generation Lag
Credit & Collections
- Average Payment Delay
- High-Risk Customers
- Collection Efficiency
Customer Intelligence
- Top Revenue Customers
- Low Margin Customers
- Payment Behavior Segmentation
Data_samples/ → Raw operational data etl/ → Python ETL pipeline Docs/ → Case study explanation Screenshots/ → Proof of pipeline execution PowerBI Dashboard/ → Final BI output requirements.txt → Project dependencies
| Layer | Technology |
|---|---|
| Storage | Azure Blob Storage |
| Processing | Python |
| Data Warehouse | SQL |
| Data Modeling | Star Schema |
| Analytics | Power BI |
| Transformation | ETL Validation Logic |
• Realistic business analytics workflow
• Data engineering fundamentals
• Data quality validation
• Dimensional modeling
• SQL analytics design
• Business KPI reporting
• Converting raw data into decision intelligence
Refer /Screenshots folder for:
- data ingestion
- ETL execution
- table loading
- schema model
- Power BI dashboard
The dashboard provides management visibility into revenue realization, customer payment behavior, and operational efficiency across the Order-to-Cash cycle.
Users can:
- monitor monthly revenue performance
- identify delayed payments
- track shipment efficiency
- analyze customer contribution
This project replicates how an organization builds an internal analytics platform to track revenue realization, operational efficiency, and customer payment behavior from operational transaction data.