How to Build an ETL Pipeline with Python and MySQL

In today’s data-driven world, extracting, transforming, and loading (ETL) data is a cornerstone of modern analytics and application development. Whether you’re aggregating customer data, preparing datasets for machine learning, or building a data warehouse, ETL pipelines are the backbone of every robust data infrastructure. This tutorial will guide you through building a complete ETL pipeline using Python and MySQL —two of the most widely used tools in the data ecosystem. By the end of this article, you’ll have a working ETL pipeline that extracts data from a source (like a CSV file), transforms it using Python, and loads it into a MySQL database. We’ll cover everything from setup to implementation, with practical code examples and actionable best practices. Prerequisites Before diving into the tutorial, ensure your environment meets the following requirements: Software and Tools Python 3.8+ installed on your machine (check with python --version ) MySQL 8.0+ (you can use MySQL Communit