Data Warehouse and Analytics Project

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.

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📖 Project Overview

This project involves:

1. Data Architecture: Designing a Modern Data Warehouse Using Medallion Architecture Bronze, Silver, and Gold layers.
2. ETL Pipelines: Extracting, transforming, and loading data from source systems into the warehouse.
3. Data Modeling: Developing fact and dimension tables optimized for analytical queries.
4. Analytics & Reporting: Creating SQL-based reports and dashboards for actionable insights.

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🚀 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.

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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.

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🏗️ Data Architecture

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

1. Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.
2. Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.
3. Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.

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📂 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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