# Introduction

Have you ever been in a meeting where a marketing director casually throws around terms like "DSPs," "third-party cookies," "header bidding," and "programmatic guaranteed," and you, a perfectly capable data engineer, nod along like you totally understand? Been there, done that.

You know data. You can wrangle Spark clusters, build ETL pipelines in your sleep, and probably explain database normalization to your neighbor's dog. But AdTech? That's a different beast entirely.

The adtech ecosystem is massive, the jargon is thick enough to cut with a knife, and every conversation with marketing teams feels like we're speaking different dialects of the same language. 

They know *what* needs to happen ("We need to retarget users who abandoned their cart"), but the data processing behind it? That's where things get fuzzy. And on our side? We know how to move terabytes of data around, but understanding *why* an impression needs to be counted within 24 hours or what makes a "viewable" impression different from a regular one? That requires translation.

As AI and automation continue to grow, the role of adtech in the digital marketing landscape is becoming more complex and harder to understand.

# Why this primer?

The goal of this primer is to provide a quick guide to adtech, from the basics to complex concepts for data engineers who quickly need to understand the basics of adtech. 

# Who is this primer for?

 This is not a manual on google analytics or about understanding P-max, just a simple guide to the adtech ecosystem and code to help you understand the concepts.

# What do you need to know to read this primer?

Python is used to write the code examples in this primer. You should be comfortable with Python and have a basic understanding of the language. Apart from that, an interest to learn about the adtech ecosystem and the code will get you a long way. The python code will be updated as time permits to make sure its still relevant.

# How is this primer structured?

This primer is structured into the following chapters with more to follow as the ecosystem evolves:

# Copyright

While the primer is free, it is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. Please attribute the work to the author.

Part 1 - Foundations of Digital Advertising & Measurement
Introduction
Chapter 2: Core Concepts
Chapter 3: Measurement Fundamentals
Chapter 4: User Identification: Cookies, Crumbling Cookies, Alternative IDs, Device vs People

Part 2 - Programmatic Advertising & Data Activation
Chapter 5: What is Programmatic Advertising
Chapter 6: RTB
Chapter 7: Programmatic Players
Chapter 5: Introduction to Programmatic Advertising
Chapter 6: Data in AdTech

Part 3 - Modern Challenges & Future Trends
Chapter 8: Data Types: 1st, 2nd, and 3rd Party
Chapter 9: DMPs
Chapter 10: CDPs
Chapter 11: Segmentation and Targeting
Chapter 7: Privacy and Regulation
Chapter 8: Programming in AdTech
Chapter 9: AI & Machine Learning in AdTech
++ More to follow as the ecosystem evolves ++

Appendices
Conclusion
Glossary