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SQL_Datawarehouse_Project

Building a modern data warehouse with SQL Server, including ETL processes, data modeling and analytics.

🚀 Project Requirements

Building the Data Warehouse (Data Engineering)

Objective

Develop a modern data warehouse using SQL Server to consolidate sales data, enabling analytical reporting and informed decision-making.

Specifications

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 a single, user-friendly data model designed for analytical queries. Scope: Focus on the latest dataset only; historization of data is not required. Documentation: Provide clear documentation of the data model to support both business stakeholders and analytics teams.


BI: Analytics & Reporting (Data Analysis)

Objective

Develop SQL-based analytics to deliver detailed insights into: Customer Behavior Product Performance Sales Trends

These insights empower stakeholders with key business metrics, enabling strategic decision-making.


For more details, refer to docs/requirements.md.

📂 Repository Structure

data-warehouse-project/ │ ├── datasets/ # Raw datasets used for the project (ERP and CRM data) │ ├── docs/ # Project documentation and architecture details │ ├── etl.drawio # Draw.io file shows all different techniquies and methods of ETL │ ├── data_architecture.drawio # Draw.io file shows the project's architecture │ ├── data_catalog.md # Catalog of datasets, including field descriptions and metadata │ ├── data_flow.drawio # Draw.io file for the data flow diagram │ ├── data_models.drawio # Draw.io file for data models (star schema) │ ├── naming-conventions.md # Consistent naming guidelines for tables, columns, and files │ ├── scripts/ # SQL scripts for ETL and transformations │ ├── bronze/ # Scripts for extracting and loading raw data │ ├── silver/ # Scripts for cleaning and transforming data │ ├── gold/ # Scripts for creating analytical models │ ├── tests/ # Test scripts and quality files │ ├── README.md # Project overview and instructions ├── LICENSE # License information for the repository ├── .gitignore # Files and directories to be ignored by Git └── requirements.txt # Dependencies and requirements for the project

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Building a modern data warehouse with SQL Server, including ETL processes, data modeling and analytics.

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