Building A Modern Data Warehouse With SQL Server , including ETL Processes , Data Modeling , And Analytics.
This project demonstrates a comprehensive data warehousing and analytics solution, from building a data warehouse to generating actionable insights. Designed as a portfolio project, it highlights industry best practices in data engineering and analytics.
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.
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).
- SQL Server Express : Lightweight server for hosting your SQL database.
- SQL Server Management Studio (SSMS) : GUI for managing and interacting with databases.
- Git Repository : Set up a GitHub account and repository to manage, version, and collaborate on your code efficiently.
- DrawIO : Design data architecture, models, flows, and diagrams.
- Notion : Get the Project Template from Notion
- Notion Project Steps : Access to All Project Phases and Tasks.
Develop a modern data warehouse using 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 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.
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.
data-warehouse-project/
│ ├── datasets/ # Raw datasets used for the project (ERP and CRM data)
│ ├── docs/ # Project documentation and architecture details
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│ ├── 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
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│ ├── scripts/ # SQL scripts for ETL and transformations
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│ ├── bronze/ # Scripts for extracting and loading raw data
│ ├── silver/ # Scripts for cleaning and transforming data
│ ├── gold/ # Scripts for creating analytical models
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│ ├── tests/ # Test scripts and quality files
│ ├── README.md # Project overview and instructions
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├── LICENSE # License information for the repository
├── .gitignore # Files and directories to be ignored by Git
└── requirements.txt # Dependencies and requirements for the project
This project is licensed under the MIT License. You are free to use, modify, and share this project with proper attribution.