SQL-Portfolio

SQL journey from foundations to production: window functions, CTEs, stored procedures, and real-world data cleaning projects

SQL Portfolio

Practical SQL from foundations to production patterns. This repository documents my SQL learning journey and showcases production-grade data cleaning and analysis projects.

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📂 Structure

sql-portfolio/
├── foundations/         # Core SQL concepts
├── intermediate/        # Joins, subqueries, string functions
├── advanced/           # CTEs, window functions, stored procedures
└── projects/
    └── layoffs_2023/   # 🌟 Featured Project: Data cleaning + EDA

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🌟 Featured Project: Global Layoffs 2023

Complete data pipeline: Raw data → Production-ready dataset → Insights

Skills Demonstrated:

• Data cleaning with CTEs and window functions
• Exploratory data analysis with complex aggregations
• Documentation and data quality standards

👉 View Project

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📚 Learning Path

Foundations

• SELECT, WHERE, GROUP BY, ORDER BY
• LIMIT, ALIAS, HAVING vs WHERE

Intermediate

• All types of JOINs (INNER, LEFT, RIGHT, FULL)
• String functions and manipulation
• Subqueries and CASE statements
• Window functions (RANK, ROW_NUMBER, DENSE_RANK)

Advanced

• Common Table Expressions (CTEs)
• Temporary tables
• Stored procedures
• Triggers and events

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🚀 How to Use

Prerequisites:

• PostgreSQL, MySQL, or BigQuery access
• Basic SQL knowledge

Run the layoffs project:

1. Load layoffs.csv to your database
2. Execute 01_data_cleaning.sql
3. Execute 02_eda.sql
4. Review insights in comments

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💡 Key Highlights

Dense rank vs row_number:

-- See advanced/14_window_functions_rank_rownum.sql
ROW_NUMBER() -- Sequential numbering, no ties
RANK()       -- Gaps after ties
DENSE_RANK() -- No gaps after ties

CTE chains for auditable cleaning:

-- See projects/layoffs_2023/01_data_cleaning.sql
WITH duplicates_flagged AS (...),
     cleaned_companies AS (...),
     standardized_industries AS (...)
SELECT * FROM standardized_industries;

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📈 Next Steps

• Add performance tuning notes (EXPLAIN plans)
• Implement data quality unit tests
• Add real-time data pipeline examples
• Build dbt transformation models

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📫 Questions?

Feel free to open an issue or reach out via LinkedIn

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