SQL Portfolio for Quality Assurance

Welcome to my SQL portfolio focused on Quality Assurance and Data Validation. This repository contains various SQL queries used for data validation, ensuring data quality, and performing database testing.

📋 Table of Contents

• About This Portfolio
• Project Structure
• Data Validation Queries
• Quality Assurance Scenarios
• Database Testing
• Tools & Technologies
• How to Use

🎯 About This Portfolio

As a Quality Assurance professional, I use SQL for:

• Data Validation: Validating data integrity and consistency
• Quality Testing: Identifying anomalies and inconsistencies
• Performance Testing: Analyzing query and database performance
• Regression Testing: Ensuring changes don't break existing data
• Compliance Checking: Verifying data meets business rules

📁 Struktur Project

SQL-Portfolio/
├── README.md
├── data-validation/
│   ├── data-integrity-checks.sql
│   ├── business-rules-validation.sql
│   └── referential-integrity.sql
├── quality-assurance/
│   ├── duplicate-detection.sql
│   ├── null-value-analysis.sql
│   └── data-consistency-checks.sql
├── performance-testing/
│   ├── query-optimization.sql
│   └── index-analysis.sql
├── test-scenarios/
│   ├── user-acceptance-tests.sql
│   └── regression-tests.sql
├── sample-data/
│   └── test-database-setup.sql
└── documentation/
    ├── testing-methodology.md
    └── query-explanations.md

🔍 Key Features

Data Validation

• ✅ Null value detection
• ✅ Data type validation
• ✅ Range and constraint checking
• ✅ Format validation (email, phone, etc.)
• ✅ Cross-table validation

Quality Assurance

• ✅ Duplicate record identification
• ✅ Data consistency verification
• ✅ Business rule compliance
• ✅ Referential integrity checks
• ✅ Statistical analysis

Testing Scenarios

• ✅ User acceptance test queries
• ✅ Regression test cases
• ✅ Edge case validation
• ✅ Performance benchmarking

🛠 Tools & Technologies

• Database Systems: MySQL, PostgreSQL, SQL Server, Oracle
• Testing Tools: SQL queries, Stored procedures
• Version Control: Git, GitHub
• Documentation: Markdown, SQL comments

🚀 How to Use

1. Clone this repository
2. Select the folder according to your testing needs
3. Run SQL scripts on your test database
4. Analyze results to identify issues
5. Document findings

📊 Sample Test Results

Each query includes:

• Expected results
• Actual results comparison
• Pass/Fail criteria
• Recommendations for fixes

📞 Contact

If you have questions or want to discuss quality assurance and SQL testing, please contact me through:

• Email: [adityadwic.career@gmail.com]
• LinkedIn: [https://www.linkedin.com/in/adityadwicahyono/]
• GitHub: [https://github.com/adityadwic]

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Note: This portfolio is continuously updated with new queries and testing scenarios following best practices in Quality Assurance.