Project Overview
An end-to-end ETL (Extract, Transform, Load) data engineering project that processes wealth-related data from CSV sources and loads it into a structured SQLite database for analysis.
Pipeline Steps
- Extract: Load raw CSV datasets containing wealth data into Pandas DataFrames
- Transform: Clean column names, handle nulls, enforce data types, and restructure for relational storage
- Load: Write transformed data into a SQLite3 database with properly defined schemas and tables
- Query: Use ipython-sql magic commands directly in Jupyter to validate and explore the loaded data
Key Skills Demonstrated
This project demonstrates the foundational data engineering skill set: understanding data contracts, writing idempotent transformations, and delivering queryable, reliable data to downstream consumers — in this case, an in-notebook analytics environment.
Outcome
A fully functional ETL pipeline runnable end-to-end in a Jupyter notebook, with clean data queryable via SQL immediately after load.