https://github.com/merrill007/sql-data-warehouse-project

The Data Warehouse and Analytics Project is a comprehensive initiative designed to demonstrate the end-to-end process of building a modern data warehouse and deriving actionable insights through SQL-based analytics.
https://github.com/merrill007/sql-data-warehouse-project

architecture business-intelligence crm data data-analysis database database-management datawarehouse erp etl etl-pipeline model sql sqlserver

Last synced: 10 months ago
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The Data Warehouse and Analytics Project is a comprehensive initiative designed to demonstrate the end-to-end process of building a modern data warehouse and deriving actionable insights through SQL-based analytics.

• Host: GitHub
• URL: https://github.com/merrill007/sql-data-warehouse-project
• Owner: Merrill007
• License: mit
• Created: 2025-03-16T07:50:27.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-03-16T20:12:11.000Z (10 months ago)
• Last Synced: 2025-03-16T21:26:15.149Z (10 months ago)
• Topics: architecture, business-intelligence, crm, data, data-analysis, database, database-management, datawarehouse, erp, etl, etl-pipeline, model, sql, sqlserver
• Homepage: https://splendoranalytics.co/
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 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.

---

## **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.

---

## **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.

---

## **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.

---

## **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.

---

## **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.

---

## **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 |

---

## **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.

---

## **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. |

---

## **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. |

---