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.
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.
🎯 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
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.
- 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.
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.
For more details, refer to docs/requirements.md.
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
The Gold Layer represents the business-level, analytics-ready data in our architecture. It consists of dimension tables and fact tables, structured to support dashboards, KPIs, and advanced analytical use cases.
Purpose:
Stores customer details enriched with demographic and geographic data.
| Column Name | Data Type | Description |
|---|---|---|
customer_key |
INT | Surrogate key uniquely identifying each customer. |
customer_id |
INT | Unique identifier assigned to each customer. |
customer_number |
NVARCHAR(50) | Alphanumeric identifier for external tracking. |
first_name |
NVARCHAR(50) | Customer's first name. |
last_name |
NVARCHAR(50) | Customer's last name. |
country |
NVARCHAR(50) | Country of residence (e.g., 'Australia'). |
marital_status |
NVARCHAR(50) | Marital status (e.g., 'Married', 'Single'). |
gender |
NVARCHAR(50) | Gender (e.g., 'Male', 'Female', 'n/a'). |
birthdate |
DATE | Date of birth (format: YYYY-MM-DD). |
create_date |
DATE | Timestamp of customer record creation. |
Purpose:
Contains product information and attributes for categorization and analysis.
| Column Name | Data Type | Description |
|---|---|---|
product_key |
INT | Surrogate key for each product record. |
product_id |
INT | Unique internal product identifier. |
product_number |
NVARCHAR(50) | Alphanumeric product code. |
product_name |
NVARCHAR(50) | Product name with key attributes (type, color, size). |
category_id |
NVARCHAR(50) | Identifier for product category. |
category |
NVARCHAR(50) | Broad classification (e.g., 'Bikes', 'Components'). |
subcategory |
NVARCHAR(50) | Detailed classification within the category. |
maintenance_required |
NVARCHAR(50) | Indicates if maintenance is required ('Yes', 'No'). |
cost |
INT | Base product cost in whole currency units. |
product_line |
NVARCHAR(50) | Product series (e.g., 'Road', 'Mountain'). |
start_date |
DATE | Launch date of the product. |
Purpose:
Stores transactional sales data linked to products and customers.
| Column Name | Data Type | Description |
|---|---|---|
order_number |
NVARCHAR(50) | Unique order identifier (e.g., 'SO54496'). |
product_key |
INT | Foreign key linking to dim_products. |
customer_key |
INT | Foreign key linking to dim_customers. |
order_date |
DATE | Date the order was placed. |
shipping_date |
DATE | Date the order was shipped. |
due_date |
DATE | Date payment was due. |
sales_amount |
INT | Total value of the sale (in whole units). |
quantity |
INT | Number of product units ordered. |
price |
INT | Price per product unit. |
✅ Note: All foreign keys (product_key, customer_key) refer to corresponding dimension tables to support a star schema structure.
- Name-Vaibhav Anand
- email_id-anandvaibhav02@gmail.com
Let's stay in touch! Feel free to connect with me on the following platforms: