This project presents the implementation of a Data Quality framework within a Data Warehouse for an omnichannel retail company.
The analysis was conducted through a thorough understanding and tracing of the conceptual, dimensional, and logical data models.
Based on this mapping, data quality rules were defined in alignment with business requirements and constraints.
Validations were automated using Great Expectations, enabling continuous monitoring and the generation of auditable reports.
The result is a reliable, governable data foundation ready to support strategic decision-making and advanced analytics initiatives.


📋 ELETRODEUTSCH DATA QUALITY PROJECT
├── 📖 Project Complete Documentation [Data Analysis Complete Project.pdf]
├── 📖 Data Quality Framework Documentation [Data Quality Framework Deployment and Execution.pdf]
├── 🏗️ Physical Data Warehouse Model [02-physical-model-project-eletrodeutsch.sql]
├── 🔄 ETL Implementation Scripts [03_etl_project-eletrodeutsch.sql]
├── 🐳 Docker Container Setup [container_docker-creation.txt]
├── 💻 Data Quality Validation Script [validation.py]
├── ⚙️ Project Dependencies [requirements.txt]
├── 📊 Data Quality Validation Reports
│ ├── [validation_report_dim_customer.html]
│ ├── [validation_report_dim_product.html]
│ ├── [validation_report_dim_channel.html]
│ └── [validation_report_fact_sales.html]
└── 🎥 Project Overview Video [report-overwie.mp4]