Creating data flow diagrams for PostgreSQL
A production-ready Python library for tabular data pipelines. Clean, detect anomalies, generate reports, and analyze SQL queries — without a database connection.
GenAI-SQL is a modular, extensible suite of AI-powered tools for automating SQL code improvement, documentation, and validation. Built for developers, analysts, and data engineers, it leverages Azure OpenAI (GPT-4o) to analyze, refactor, comment, explain, test, and audit SQL — all within a secure, asynchronous, and HIPAA-compliant framework.
This project uses the S.Y. 2020-2021 DepEd Schools Masterlist that contains 64,000+ school information across the Philippines, including location, sectors, and classification details.
Self-updating analytics platform comparing Pakistan's data/analytics job market against the global market — live web scraping, PostgreSQL, SQL analysis, ML, and an auto-refreshing Streamlit dashboard deployed to the cloud.
A new package that helps developers ensure column safety in SQLite queries by analyzing and validating their SQL statements. The package takes a user's SQL query as text input and returns a structured
Using SQL and Python, I analyzed a FIFA World Cup database covering both Men's and Women's tournaments. By querying data on teams, players, matches, goals, and bookings, I uncovered insights into scoring trends, tournament records, player demographics, disciplinary patterns, host nation performance, and historical comparisons between competitions
Insurance claims analytics platform analyzing claim costs, risk patterns, fraud indicators, and operational KPIs using SQL, Python, Tableau, and Streamlit to support data-driven insurance decisions.
🔍 Validate SQLite queries with sqlite-column-sentry to catch column-related issues early and enhance data safety in your Python projects.
A complete, end-to-end product analytics system built to demonstrate the skills — SQL depth, metric design, cohort thinking, A/B test evaluation, and analytical storytelling.
End-to-end data analytics pipeline analysing AI market trends across industries, geographies and cloud platforms (2019–2025) | Python · MySQL · Power BI
End-to-end data analytics project tracking global AI adoption across 14 countries, 8 industries & 4 cloud providers (2019–2025) using Python, MySQL and Power BI.
End-to-end SQL Data Pipeline: Data Modeling (Star Schema), Automated Data Seeding, and Advanced Analytics using CTEs, Views, and Stored Procedures.
Local-first warehouse FinOps query optimizer with synthetic query history, SQL anti-pattern detection, advisory cost modeling, dbt-style graph analysis, recommendations, metrics, and CI.
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