Data Warehouse and Analytics Project
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.
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: Everything is for Free!
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.
🚀 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.
📂 Repository Structure data-warehouse-project/ │ ├── datasets/ # Raw datasets used for the project (ERP and CRM data) │
│ ├── 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 │
│ ├── 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 Hi there! Omomoh Daniel, also known as dhn_illuminate on X. I’m an IT professional and passionate about data journey from ingestion and cleaning to visualization!