Building a modern data warehouse with SQL Server, including ETL processes, data modeling, and analytics
Welcome to the Data Warehouse and Analytics Project repository This project demonstrates a comprehensive data warehousing and analytics solution, from building a data warehouse to generating actionable insights.
🏗️ Data Architecture The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers:
Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database. Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis. Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.
📖 Project Overview This project involves:
Data Architecture: Designing a Modern Data Warehouse Using Medallion Architecture Bronze, Silver, and Gold layers. ETL Pipelines: Extracting, transforming, and loading data from source systems into the warehouse. Data Modeling: Developing fact and dimension tables optimized for analytical queries. Analytics & Reporting: Creating SQL-based reports and dashboards for actionable insights.
Datasets: Access to the project dataset (csv files).
Project Requirements Building the Data Warehouse 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