This project showcases an end-to-end data warehousing and analytics solution, built using modern data engineering practices. It demonstrates how raw operational data is transformed into a trusted, governed, and analytics-ready data warehouse designed for business insights.
Designed as a portfolio project, it highlights industry-standard approaches in data engineering and analytics.
The goal of this project is to simulate a real-world analytics environment - from ingesting raw data, building a scalable warehouse, and creating dashboards—to show strong competency in data engineering, analytics engineering, and BI.
Develop a modern data warehouse using the SQL Server to consolidate sales data, enabling analytical reporting and informed decision-making.
- Data Sources : Import data from two source systems (ERP and CRM) provided as csv files.
- Data Quality : Cleanse and resolve data quality issues prior to analysis.
- Integration : Combine both sources into single, user friendly data model designed for analytical queries.
- Scope : Focus on the latest dataset only; historization not included.
- Documentation : Provide clear documentation of the data model to support both business stakeholders and analytics team.
Develop SQL-based analytics to deliver detailed insights into
- Customer Behaviour
- Product Performance
- Sales Trend
These insights empower stakeholders with key business metrics , enabling strategic decision making.
This project is licensed under the [MIT License].(LICENSE).