Python Data Engineering: Build an ETL Pipeline Project

Transitioning from basic Python syntax to real-world data engineering can feel daunting without a practical project to tie everything together. This text-based course guides you through building a functional data pipeline from scratch. You will step into the role of a data engineer, learning how to gather messy data from web pages and APIs, clean and transform it using modern Python libraries, and store it securely in a structured database. By reading through practical explanations and studying clear code snippets, you will gain the confidence to design your own ETL (Extract, Transform, Load) pipelines. What you'll learn: - Understand foundational data engineering concepts, ETL pipelines, and essential terminology before writing code. - Extract data from diverse sources using web scraping techniques and API integrations. - Transform raw datasets using modern data libraries, applying type hints for cleaner code and robust data schemas. - Load processed data into structured databases, ensuring integrity and proper formatting. - Implement structured logging and basic error handling to keep your pipeline running smoothly. The course begins with core definitions and architectural concepts before moving step-by-step through extraction, transformation, and loading phases. You will read through detailed code implementations and logical explanations designed to show you how professional pipelines are structured. This course is designed for aspiring data engineers and Python beginners who want to apply their programming knowledge to real-world data tasks. No prior data engineering experience is required, as we start with the absolute basics. Start reading today to build your first production-ready data pipeline.