📌 # Retail Data Platform & Analytics Project End-to-end retail data project covering ingestion, processing, data governance and business analytics using Azure, SQL and Power BI.

1️⃣ Project Overview This project implements an end-to-end retail data platform, starting from raw sales data ingestion and ending with analytical reporting and data governance visualization.

The main objective is to demonstrate a complete data lifecycle:

• Data ingestion and storage
• Data cleaning and transformation
• Aggregation and analytical modeling
• Data quality validation
• Data lineage and governance
• Business intelligence reporting

The project has been designed with a strong focus on clarity, traceability and reproducibility, simulating a real-world data engineering and analytics scenario.

2️⃣ High-Level Architecture

The following diagram illustrates the global architecture of the project, including data sources, processing layers, governance and analytics.

3️⃣ Data Sources The project is based on a retail dataset containing:

• Sales transactions
• Products information
• Customers information

The data is initially stored in a relational format and represents a typical transactional retail system.

4️⃣ Data Processing & Modeling Data processing is implemented using SQL-based transformations, including:

• Raw data ingestion
• Data cleaning and normalization
• Creation of fact and dimension tables
• Generation of aggregated tables for analytical purposes

Different levels of aggregation are used to optimize analytical queries and reporting performance. The data model follows a star-schema approach, separating fact and dimension tables to support analytical workloads.

5️⃣ Data Governance & Lineage Data governance is addressed through dataset and process lineage documentation.

The project includes:

• Identification of datasets and processes
• Definition of inputs and outputs
• Visualization of lineage relationships

These elements allow understanding how data flows through the system and how analytical outputs are derived from source datasets.

6️⃣ Analytics & Reporting The analytical layer is implemented in Power BI, including:

• Business KPIs and sales analysis
• Data quality validation checks
• Aggregated and detailed views
• Data lineage visualizations

The Power BI report is structured into the following sections:

• Business performance analysis
• Data quality validation
• Data lineage and governance

🔗 Power BI Report: https://app.powerbi.com/view?r=eyJrIjoiZDU3MDMzZTEtNDJmYy00NTBkLWIxNGMtMWZiNjBkNmU2NzhkIiwidCI6ImJlNDY1NWRmLWFjNzMtNDAxZi1hN2FlLTE5OGMzYjcyZDBjNiIsImMiOjh9

7️⃣ Repository Structure /docs

• Project documentation and architecture diagrams

/data

• Sample or reference datasets used in the project

/sql

• SQL scripts for data ingestion, transformation and aggregation

/python

• Python notebooks or scripts for additional analysis or experimentation

/atlas

• Metadata and lineage information exported from Apache Atlas

/powerbi

• Power BI report files (.pbix)

8️⃣ Technologies Used

• SQL Server
• Python / Jupyter Notebooks (ETL & integration)
• API Flask
• Azure SQL Database
• Azure Blob Storage / Azure Data Factory
• Apache Atlas (Data Governance & Lineage)
• Power BI (Desktop & Service)
• Git & GitHub
• draw.io (architecture diagrams)

Final Notes

This project was designed as a hands-on exercise to cover the full lifecycle of a modern data platform, from ingestion to governance and visualization.