AdTech and Data Primer

A comprehensive guide for data engineers to master Advertising Technology (AdTech) and programmatic advertising.

📖 Read the Primer

Read the full primer online here

🚀 About

This project bridges the gap between complex AdTech concepts and practical data engineering. It covers:

• Core Concepts: RTB, DSPs, SSPs, and bid streams.
• Data Engineering: Pipelines, user identification, and identity graphs.
• Privacy: GDPR, CCPA, and the future of cookie-less tracking.
• Code: Practical Python examples (Jupyter Notebooks) for building ad servers, simulations, and bidders.

🛠️ Local Development

1. Clone the repo:
2. Install dependencies:

   pip install -r book/requirements.txt

3. Build the primer:

   jupyter-book build book/

🤝 Contributing

Contributions are welcome! If you find an error, have a suggestion, or want to add a new topic, please feel free to contribute.

1. Fork the repository.
2. Clone your fork locally.
3. Create a branch for your changes (git checkout -b fix/typo-in-chapter-1).
4. Make your changes (edits to markdown files or notebooks).
5. Commit your changes (git commit -m "Fix typo in intro chapter").
6. Push to your fork (git push origin fix/typo-in-chapter-1).
7. Open a Pull Request against the main branch of this repository.

Contributors

If you have contributed to this project, please add your name below:

• Add your name here

📄 License

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).