SQL for QA Testing

A comprehensive SQL query collection focused on data quality validation and bug detection in databases. This project demonstrates real-world QA skills: finding data anomalies, validating integrity, and detecting broken relationships.

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🎯 Project Purpose

This repository showcases SQL skills from a QA Engineer perspective:

• ✅ Detecting invalid data (negative values, nulls, zeros)
• ✅ Finding duplicates and inconsistencies
• ✅ Validating data integrity and relationships
• ✅ Identifying orphaned records and broken references

This is NOT about writing complex queries to show off.
This IS about writing clear, focused queries that find real bugs.

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🗄️ Database Schema

Simple e-commerce database with intentional bugs for QA practice:

Tables:

• users - Customer accounts
• products - Product catalog
• orders - Purchase orders

Relationships:

orders.user_id    → users.id
orders.product_id → products.id

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🐛 Intentional Bugs in Dataset

The seed data contains real-world bugs that QA engineers encounter:

Data Quality Issues:

• Negative totals and prices
• Zero prices and quantities
• Invalid/future dates
• Empty/null required fields

Duplicates:

• Duplicate emails in users
• Potential duplicate orders

Data Integrity:

• Orphaned orders (user doesn't exist)
• Broken references (product doesn't exist)
• Totals that don't match calculations

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📂 Project Structure

sql-for-qa-testing/
├── schema/
│   └── schema.sql              # Database structure (simple & clear)
├── data/
│   └── seed_data.sql           # Test data with intentional bugs
├── queries/
│   ├── 01_basic_queries.sql    # Simple filters and aggregations
│   ├── 02_joins.sql            # Find missing relationships
│   └── 03_edge_cases.sql       # Real-world scenarios
├── results/
│   └── bug_findings.md         # Document what you found
└── README.md

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🚀 Getting Started

Prerequisites

• MySQL 8.0+
• MySQL Workbench (or any SQL client)

Setup

1. Create database:

   CREATE DATABASE sql_qa_testing;
   USE sql_qa_testing;

2. Run schema:

   mysql -u root -p sql_qa_testing < schema/schema.sql

3. Load test data:

   mysql -u root -p sql_qa_testing < data/seed_data.sql

4. Start finding bugs:

   source queries/01_basic_queries.sql

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🧪 Running Queries

Each query file is standalone and can be executed independently:

-- Example: Find negative prices
source queries/02_data_quality/find_negative_totals.sql

Or open in MySQL Workbench and execute.

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📊 What You'll Learn

As a QA Engineer:

• How to validate data quality systematically
• Writing queries that detect real bugs
• Understanding data relationships and integrity
• Identifying edge cases in datasets

Technical Skills:

• SQL SELECT with filters and joins
• Aggregate functions for validation
• Subqueries for data verification
• NULL handling and data anomalies

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🎓 Query Philosophy

Good QA SQL queries are:

• ✅ Clear - Easy to understand what's being checked
• ✅ Focused - One validation per query
• ✅ Documented - Comments explain the bug being detected
• ✅ Actionable - Results can be directly reported

Avoid:

• ❌ Overly complex queries that are hard to maintain
• ❌ Multiple validations in one query
• ❌ Queries without clear purpose

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📊 Project Status

Category	Status	Queries
Basic Validation	✅ Complete	3/3
Data Quality	✅ Complete	4/4
Duplicates	✅ Complete	3/3
Data Integrity	✅ Complete	3/3

Total: 13 professional QA queries

🐛 Bugs Found

This project successfully detected:

• 3 orders with invalid totals (negative/zero)
• 2 records with future dates
• 3 products with zero/negative prices
• 1 user with empty name
• 2 duplicate emails (4 users affected)
• 1 orphaned order (user doesn't exist)
• 1 broken reference (product doesn't exist)

🎓 Learning Outcomes

QA Thinking:

• ✅ How to approach data validation systematically
• ✅ What questions to ask about data integrity
• ✅ How to prioritize which checks matter most
• ✅ Writing queries that find real bugs

SQL Skills:

• ✅ Basic SELECT with meaningful WHERE clauses
• ✅ JOINs to detect missing relationships
• ✅ Aggregations (COUNT, SUM) for validation
• ✅ NULL handling (IS NULL, COALESCE)

Not Covered (Intentionally):

• ✅ Window functions (RANK, LAG) for pattern detection
• ✅ Common Table Expressions (CTEs) for readable logic
• ✅ Stored Procedures for automated validation
• ✅ Database Views for data abstraction

Why? Professional QA Automation requires scalable and maintainable code. By using Views and Procedures, we centralize validation logic and eliminate redundant queries.

Data Integrity: 86.67% (13 out of 15 orders are valid)

🤝 Contributing

This is a personal learning project, but suggestions are welcome!

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👤 Author

• GitHub: https://github.com/ChandeDeVargas
• LinkedIn: https://www.linkedin.com/in/chande-de-vargas-b8a51838a/

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📄 License

This project is open source and available under the MIT License.

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⭐ If this project helps you learn SQL for QA, give it a star!