SQL-Data-Warehouse-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.

Full Requirement Analysis for the Data Warehouse and Analytics Project

1. Project Objective

The main goal of this project is to build a modern data warehouse using SQL Server, following the Medallion Architecture (Bronze, Silver, and Gold layers) to support business intelligence (BI) and analytics reporting. The system will:

• Integrate sales-related data from multiple sources (ERP & CRM).
• Process and cleanse data through ETL pipelines.
• Model data for analytical querying and reporting.
• Generate insights for decision-making through SQL queries and dashboards.

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2. Stakeholders & Users

The primary users of this system include:

• Data Analysts & BI Developers: Need optimized tables for quick querying and analytics.
• Data Engineers: Responsible for designing ETL pipelines and ensuring efficient data transformation.
• Business Decision-Makers: Require actionable insights from reports.
• IT & Database Administrators: Maintain the SQL Server database and ensure data integrity.

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3. Business Requirements

3.1 Key Business Objectives

• Consolidate sales, customer, and product data from ERP and CRM systems.
• Provide near real-time data updates for accurate reporting.
• Optimize data storage and retrieval using the Medallion Architecture.
• Support ad-hoc analytics with structured data models (star schema).

3.2 Expected Business Insights

• Customer Analysis: Identify high-value customers, purchase patterns, and customer retention rates.
• Product Performance: Analyze best-selling products, product trends, and sales contribution.
• Sales Trends: Identify revenue trends, seasonal variations, and regional performance.
• Operational Insights: Detect data quality issues, missing values, and inconsistencies in source systems.

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4. Functional Requirements

The system should support the following functionalities:

4.1 Data Ingestion

• Import ERP & CRM datasets (CSV files) into the SQL database.
• Maintain a raw data layer (Bronze Layer) that stores unprocessed data.
• Automate data loading using SQL Server Integration Services (SSIS) or Python scripts.

4.2 Data Cleaning & Transformation

• Standardize and clean customer, sales, and product data (Silver Layer).
• Handle missing values, duplicates, and format inconsistencies.
• Apply data validation rules to ensure consistency.

4.3 Data Integration & Modeling

• Merge ERP and CRM datasets into a single schema optimized for analytics.
• Design a Star Schema for efficient reporting:
  • Fact Table: Stores transactions (sales, revenue, order details).
  • Dimension Tables: Stores product, customer, and date information.
• Implement data transformations using T-SQL procedures.

4.4 Data Storage & Query Optimization

• Store transformed data in the Gold Layer for analytics.
• Optimize SQL queries with indexes, partitions, and materialized views.
• Maintain historical data where applicable.

4.5 Business Intelligence & Reporting

• Develop SQL-based queries to generate:
  • Customer Segmentation Reports
  • Product Performance Dashboards
  • Sales Growth Analysis
  • Revenue Forecasting
• Enable data visualization using Power BI or Tableau.

4.6 Documentation & Governance

• Maintain a Data Catalog with field definitions and relationships.
• Follow naming conventions for tables, columns, and indexes.
• Ensure auditability by tracking data transformations.

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5. Technical Requirements

5.1 Data Sources

• ERP System (sales data, orders, products).
• CRM System (customer data, interactions).
• CSV Files as the primary data exchange format.

5.2 Database

• SQL Server Express (lightweight database for development).
• SSMS (SQL Server Management Studio) for administration.

5.3 ETL Tools

• SQL Server Integration Services (SSIS) for batch processing.
• Python (Pandas, SQLAlchemy) for scripting ETL.
• Stored Procedures & SQL Jobs for data transformation automation.

5.4 Data Storage & Architecture

• Medallion Architecture:
  • Bronze Layer (Raw Data Storage)
  • Silver Layer (Cleansed & Standardized Data)
  • Gold Layer (Final Analytical Data)
• Star Schema Design:
  • Fact and Dimension Tables.
  • Optimized for OLAP querying.

5.5 Reporting & Analytics

• Power BI, Tableau for dashboard visualization.
• SQL Queries for ad-hoc reporting.
• Stored Procedures for data aggregation.

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6. Non-Functional Requirements

6.1 Performance

• Query execution should be optimized for fast retrieval (<2 sec response time for 80% queries).
• Support for handling millions of records efficiently.

6.2 Scalability

• Ability to handle growing datasets (scale horizontally if needed).
• Flexible ETL pipelines that allow easy data source expansion.

6.3 Security

• User authentication using SQL Server roles.
• Data access control based on user permissions.
• Encryption of sensitive data fields (e.g., customer email, payment info).

6.4 Reliability & Availability

• Automated backups to prevent data loss.
• Error handling mechanisms in ETL to track failed jobs.
• Logging & Monitoring to ensure data consistency.

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7. Data Model Design (Star Schema)

7.1 Fact Table: fact_sales

Column Name	Data Type	Description
sale_id	INT (PK)	Unique identifier for a sale
customer_id	INT (FK)	Customer reference
product_id	INT (FK)	Product reference
order_date	DATE	Date of sale
quantity_sold	INT	Number of products sold
unit_price	DECIMAL	Price per unit
total_sales	DECIMAL	Total revenue per sale

7.2 Dimension Tables

Customers (dim_customers)

Column Name	Data Type	Description
customer_id	INT (PK)	Unique ID of the customer
name	VARCHAR	Customer name
email	VARCHAR	Contact email
country	VARCHAR	Country of the customer

Products (dim_products)

Column Name	Data Type	Description
product_id	INT (PK)	Unique product ID
name	VARCHAR	Product name
category	VARCHAR	Category of the product

Dates (dim_dates)

Column Name	Data Type	Description
date_id	INT (PK)	Date Key
full_date	DATE	Actual date
month	INT	Month of sale
year	INT	Year of sale

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8. Deliverables

1. SQL Scripts:
   • ETL (Extract, Transform, Load) for loading data into SQL Server.
   • Queries for data cleaning and transformation.
   • SQL queries for analytics and reporting.
2. Database Schema Documentation:
   • ER Diagrams & Data Dictionary.
3. Data Visualization Dashboards:
   • Power BI or Tableau reports.
4. Repository Documentation:
   • ReadMe, data catalog, and naming conventions.

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9. Risks & Mitigation

Risk	Impact	Mitigation Strategy
Data Quality Issues	High	Implement validation checks and cleansing procedures.
Slow Query Performance	Medium	Optimize indexes and use caching strategies.
Security Breach	High	Use encryption and role-based access controls.
Data Integration Issues	Medium	Standardize formats before ingestion.

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10. Timeline & Milestones

Phase	Duration	Key Deliverables
Requirement Analysis	1 Day	Finalized document.
Data Warehouse Design	2 Days	ERD, schema, and data modeling.
ETL Development	3 Days	Data pipelines, cleansing.
Reporting & Analytics	2 Days	Dashboards, SQL queries.
Testing & Optimization	2 Days	Performance tuning.
Documentation & Release	1 Days	ReadMe, Data Catalog.

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