{"id":26562643,"url":"https://github.com/merrill007/sql-data-warehouse-project","last_synced_at":"2025-03-22T15:18:29.603Z","repository":{"id":282770816,"uuid":"949330919","full_name":"Merrill007/SQL-Data-Warehouse-Project","owner":"Merrill007","description":"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.","archived":false,"fork":false,"pushed_at":"2025-03-16T20:12:11.000Z","size":6,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-16T21:26:15.149Z","etag":null,"topics":["architecture","business-intelligence","crm","data","data-analysis","database","database-management","datawarehouse","erp","etl","etl-pipeline","model","sql","sqlserver"],"latest_commit_sha":null,"homepage":"https://splendoranalytics.co/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Merrill007.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-16T07:50:27.000Z","updated_at":"2025-03-16T20:32:33.000Z","dependencies_parsed_at":"2025-03-16T21:36:17.450Z","dependency_job_id":null,"html_url":"https://github.com/Merrill007/SQL-Data-Warehouse-Project","commit_stats":null,"previous_names":["merrill007/sql-data-warehouse-project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merrill007%2FSQL-Data-Warehouse-Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merrill007%2FSQL-Data-Warehouse-Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merrill007%2FSQL-Data-Warehouse-Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merrill007%2FSQL-Data-Warehouse-Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Merrill007","download_url":"https://codeload.github.com/Merrill007/SQL-Data-Warehouse-Project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244973809,"owners_count":20541025,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["architecture","business-intelligence","crm","data","data-analysis","database","database-management","datawarehouse","erp","etl","etl-pipeline","model","sql","sqlserver"],"created_at":"2025-03-22T15:18:28.860Z","updated_at":"2025-03-22T15:18:29.587Z","avatar_url":"https://github.com/Merrill007.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# SQL-Data-Warehouse-Project\nWelcome 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.\n\n\n## **Full Requirement Analysis for the Data Warehouse and Analytics Project**\n\n### **1. Project Objective**\nThe 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:\n- Integrate sales-related data from multiple sources (ERP \u0026 CRM).\n- Process and cleanse data through ETL pipelines.\n- Model data for analytical querying and reporting.\n- Generate insights for decision-making through SQL queries and dashboards.\n\n---\n\n## **2. Stakeholders \u0026 Users**\nThe primary users of this system include:\n- **Data Analysts \u0026 BI Developers**: Need optimized tables for quick querying and analytics.\n- **Data Engineers**: Responsible for designing ETL pipelines and ensuring efficient data transformation.\n- **Business Decision-Makers**: Require actionable insights from reports.\n- **IT \u0026 Database Administrators**: Maintain the SQL Server database and ensure data integrity.\n\n---\n\n## **3. Business Requirements**\n### **3.1 Key Business Objectives**\n- Consolidate **sales, customer, and product** data from **ERP and CRM systems**.\n- Provide **near real-time** data updates for accurate reporting.\n- Optimize data storage and retrieval using the **Medallion Architecture**.\n- Support ad-hoc analytics with **structured data models (star schema)**.\n\n### **3.2 Expected Business Insights**\n- **Customer Analysis**: Identify high-value customers, purchase patterns, and customer retention rates.\n- **Product Performance**: Analyze best-selling products, product trends, and sales contribution.\n- **Sales Trends**: Identify revenue trends, seasonal variations, and regional performance.\n- **Operational Insights**: Detect data quality issues, missing values, and inconsistencies in source systems.\n\n---\n\n## **4. Functional Requirements**\nThe system should support the following functionalities:\n\n### **4.1 Data Ingestion**\n- Import **ERP \u0026 CRM** datasets (CSV files) into the SQL database.\n- Maintain a raw data layer (**Bronze Layer**) that stores unprocessed data.\n- Automate data loading using **SQL Server Integration Services (SSIS) or Python scripts**.\n\n### **4.2 Data Cleaning \u0026 Transformation**\n- Standardize and clean customer, sales, and product data (**Silver Layer**).\n- Handle missing values, duplicates, and format inconsistencies.\n- Apply data validation rules to ensure consistency.\n\n### **4.3 Data Integration \u0026 Modeling**\n- Merge ERP and CRM datasets into a **single schema** optimized for analytics.\n- Design a **Star Schema** for efficient reporting:\n  - **Fact Table**: Stores transactions (sales, revenue, order details).\n  - **Dimension Tables**: Stores product, customer, and date information.\n- Implement data transformations using **T-SQL procedures**.\n\n### **4.4 Data Storage \u0026 Query Optimization**\n- Store transformed data in the **Gold Layer** for analytics.\n- Optimize SQL queries with **indexes, partitions, and materialized views**.\n- Maintain historical data where applicable.\n\n### **4.5 Business Intelligence \u0026 Reporting**\n- Develop SQL-based queries to generate:\n  - Customer Segmentation Reports\n  - Product Performance Dashboards\n  - Sales Growth Analysis\n  - Revenue Forecasting\n- Enable data visualization using **Power BI or Tableau**.\n\n### **4.6 Documentation \u0026 Governance**\n- Maintain a **Data Catalog** with field definitions and relationships.\n- Follow **naming conventions** for tables, columns, and indexes.\n- Ensure **auditability** by tracking data transformations.\n\n---\n\n## **5. Technical Requirements**\n### **5.1 Data Sources**\n- **ERP System** (sales data, orders, products).\n- **CRM System** (customer data, interactions).\n- **CSV Files** as the primary data exchange format.\n\n### **5.2 Database**\n- **SQL Server Express** (lightweight database for development).\n- **SSMS (SQL Server Management Studio)** for administration.\n\n### **5.3 ETL Tools**\n- **SQL Server Integration Services (SSIS)** for batch processing.\n- **Python (Pandas, SQLAlchemy)** for scripting ETL.\n- **Stored Procedures \u0026 SQL Jobs** for data transformation automation.\n\n### **5.4 Data Storage \u0026 Architecture**\n- **Medallion Architecture**:\n  - **Bronze Layer** (Raw Data Storage)\n  - **Silver Layer** (Cleansed \u0026 Standardized Data)\n  - **Gold Layer** (Final Analytical Data)\n- **Star Schema Design**:\n  - Fact and Dimension Tables.\n  - Optimized for **OLAP querying**.\n\n### **5.5 Reporting \u0026 Analytics**\n- **Power BI, Tableau** for dashboard visualization.\n- **SQL Queries** for ad-hoc reporting.\n- **Stored Procedures** for data aggregation.\n\n---\n\n## **6. Non-Functional Requirements**\n### **6.1 Performance**\n- Query execution should be optimized for **fast retrieval (\u003c2 sec response time for 80% queries)**.\n- Support for handling **millions of records** efficiently.\n\n### **6.2 Scalability**\n- Ability to handle growing datasets (scale horizontally if needed).\n- Flexible ETL pipelines that allow easy data source expansion.\n\n### **6.3 Security**\n- **User authentication** using SQL Server roles.\n- **Data access control** based on user permissions.\n- **Encryption** of sensitive data fields (e.g., customer email, payment info).\n\n### **6.4 Reliability \u0026 Availability**\n- **Automated backups** to prevent data loss.\n- **Error handling mechanisms** in ETL to track failed jobs.\n- **Logging \u0026 Monitoring** to ensure data consistency.\n\n---\n\n## **7. Data Model Design (Star Schema)**\n### **7.1 Fact Table: `fact_sales`**\n| Column Name      | Data Type | Description |\n|-----------------|----------|-------------|\n| sale_id         | INT (PK) | Unique identifier for a sale |\n| customer_id     | INT (FK) | Customer reference |\n| product_id      | INT (FK) | Product reference |\n| order_date      | DATE     | Date of sale |\n| quantity_sold   | INT      | Number of products sold |\n| unit_price      | DECIMAL  | Price per unit |\n| total_sales     | DECIMAL  | Total revenue per sale |\n\n### **7.2 Dimension Tables**\n#### **Customers (`dim_customers`)**\n| Column Name  | Data Type | Description |\n|-------------|----------|-------------|\n| customer_id | INT (PK) | Unique ID of the customer |\n| name        | VARCHAR  | Customer name |\n| email       | VARCHAR  | Contact email |\n| country     | VARCHAR  | Country of the customer |\n\n#### **Products (`dim_products`)**\n| Column Name  | Data Type | Description |\n|-------------|----------|-------------|\n| product_id  | INT (PK) | Unique product ID |\n| name        | VARCHAR  | Product name |\n| category    | VARCHAR  | Category of the product |\n\n#### **Dates (`dim_dates`)**\n| Column Name  | Data Type | Description |\n|-------------|----------|-------------|\n| date_id     | INT (PK) | Date Key |\n| full_date   | DATE     | Actual date |\n| month       | INT      | Month of sale |\n| year        | INT      | Year of sale |\n\n---\n\n## **8. Deliverables**\n1. **SQL Scripts**:\n   - ETL (Extract, Transform, Load) for loading data into SQL Server.\n   - Queries for data cleaning and transformation.\n   - SQL queries for analytics and reporting.\n2. **Database Schema Documentation**:\n   - ER Diagrams \u0026 Data Dictionary.\n3. **Data Visualization Dashboards**:\n   - Power BI or Tableau reports.\n4. **Repository Documentation**:\n   - ReadMe, data catalog, and naming conventions.\n\n---\n\n## **9. Risks \u0026 Mitigation**\n| **Risk**                | **Impact** | **Mitigation Strategy** |\n|-------------------------|-----------|-------------------------|\n| Data Quality Issues     | High      | Implement validation checks and cleansing procedures. |\n| Slow Query Performance  | Medium    | Optimize indexes and use caching strategies. |\n| Security Breach         | High      | Use encryption and role-based access controls. |\n| Data Integration Issues | Medium    | Standardize formats before ingestion. |\n\n---\n\n## **10. Timeline \u0026 Milestones**\n| **Phase**               | **Duration** | **Key Deliverables** |\n|-------------------------|-------------|----------------------|\n| Requirement Analysis    | 1 Day      | Finalized document. |\n| Data Warehouse Design  | 2 Days     | ERD, schema, and data modeling. |\n| ETL Development        | 3 Days     | Data pipelines, cleansing. |\n| Reporting \u0026 Analytics  | 2 Days     | Dashboards, SQL queries. |\n| Testing \u0026 Optimization | 2 Days     | Performance tuning. |\n| Documentation \u0026 Release | 1 Days      | ReadMe, Data Catalog. |\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmerrill007%2Fsql-data-warehouse-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmerrill007%2Fsql-data-warehouse-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmerrill007%2Fsql-data-warehouse-project/lists"}