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. Designed as a portfolio project, it highlights industry best practices in data engineering and analytics.

🏗️ Data Architecture The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers: Data Architecture

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.

🎯 This repository is an excellent resource for professionals and students looking to showcase expertise in:

SQL Development Data Architect Data Engineering ETL Pipeline Developer Data Modeling Data Analytics

🛠️ Important Links & Tools:

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: All-in-one tool for project management and organization. Notion Project Steps: Access to All Project Phases and Tasks.

🚀 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

🛡️ License

This project is licensed under the MIT License. You are free to use, modify, and share this project with proper attribution.

🌟 About Me

I`m Rusu Adrian studying data analysis