A Python-based ETL pipeline that extracts personal finance transaction data from CSV files, transforms it using pandas, and loads it into a PostgreSQL database with star schema modeling. Demonstrates core data engineering fundamentals with proper error handling and data quality checks.

Simple ETL pipeline using Apache Airflow to input data from CSV to local PostgreSQL

A local, end-to-end retail analytics data warehouse that ingests Olist e-commerce CSV data, transforms it through a Python ETL pipeline orchestrated by Apache Airflow, loads it into a PostgreSQL star schema, and serves insights via FastAPI — all running on Docker with zero cloud dependencies.

An ETL pipeline transferring data to a PostgreSQL database. Source data are in Ms Excel, they are extracted and transformed in Python, Pandas and alternative Python dictionary methods, regular expressions. Four CSV files are created and from the normalized datasets, an ERD and table schema developed aimed at modeling data for a local business.