End-to-end data engineering and analytics project using Azure Data Factory and SQL
📁 SQL queries used in this analysis are available in the /sql folder.
This project uses data warehousing and SQL analytics to support product development decisions for Adventure Works.
The focus is to identify high-performing products, uncover cost inefficiencies, and improve profitability through data-driven insights.
A star schema data warehouse was designed to integrate product, sales, and quality data into a structured model for analysis.
An automated ETL pipeline was built using Azure Data Factory to extract, transform, and load data from multiple sources into the data warehouse.
- Compared production cost and profit across product categories
- Identified high-cost products with low returns
- Evaluated in-house vs outsourced production performance
🔗 View SQL
- Analysed sales and profit distribution across regions
- Identified regions with strong revenue but weak profitability
- Highlighted opportunities for pricing and inventory optimisation
🔗 View SQL
- Evaluated performance across product variations (color, style, type)
- Identified customer preferences and underperforming configurations
- Supported product portfolio optimisation decisions
🔗 View SQL
- Analysed scrap cost and defect patterns across products
- Identified manufacturing and operational inefficiencies
- Highlighted key drivers of hidden production costs
🔗 View SQL
- High production cost does not always lead to high profitability
- Product performance varies significantly across regions and configurations
- Scrap waste is a critical but often hidden cost driver
- Data-driven analysis supports more effective product and operational decisions
This project was completed as part of a group case study. My contribution focused on:
- data warehouse schema design (star schema)
- supporting ETL processes using Azure Data Factory and SQL
- data modelling and analytical interpretation
- identifying business insights to support product and operational decisions
This project was completed as part of a group assignment at the University of Auckland and is shared for educational and portfolio purposes only. The work presented reflects collaborative academic effort. Unauthorised use, reproduction, or redistribution may violate academic integrity policies.