by freeCodeCamp, featuring Alexey Grigoriev and Adrian Brudaru
Learn how to build modern, scalable data pipelines using Python and AI-assisted tools. This hands-on tutorial starts from the basics of data ingestion and takes you all the way to advanced techniques in data loading, transformation, deployment, and automation.
In the first part, Alexey walks you through the traditional challenges of building data pipelines from scratch—handling nested API calls, validating and transforming raw data, defining dynamic schemas, and storing data in database. You'll see how much boilerplate and custom code is typically involved in getting a basic ETL pipeline to work.
Then in the second part, Adrian introduces dlt (data load tool) — an open-source library that simplifies and automates the hard parts of data engineering.
You'll learn how to:
- Extract and normalize data from APIs and databases with minimal code
- Manage schema evolution and enforce data contracts
- Load data into warehouses, databases, and data lakes
- Handle incremental loads, backfilling, and slowly changing dimensions (SCD2)
- Optimize performance and choose the right write strategies
- Deploy pipelines using tools like Crontab, GitHub Actions, Airflow, and Dagster
- Leverage Large Language Models (LLMs) to generate pipelines and documentation
Whether you're a beginner looking to understand how data flows through pipelines, or an experienced engineer wanting to automate and scale your data workflows, this course will give you the skills and tools to do it efficiently — with clean, maintainable Python.
- Youtube video: https://youtu.be/T23Bs75F7ZQ
- Google Colab Notebooks:
- Part 1: Introduction into Data Ingestion
- Part 2: Data Ingestion with dlt
Watch the video, follow the Colab Notebook, take a quiz and get certified!
Follow the link: https://dlthub.learnworlds.com/course/data-engineering-with-python-and-ai