I remember the good ‘ol days where cookies were that little text file you ignored while debugging a frontend issue. If you’re a data engineer like me, you’ve probably spent the last decade happily ignoring the specific mechanics of how users are tracked across the web. You built the pipelines, you ingested the logs, and you let the marketing team worry about other stuff. But recently, the din about “Cookie Apocalypse” and “Hyper Personalization using AI’ is like a leaf blower with a megaphone attached. This is fast becoming a data engineering problem.
Without cookies, we are entering the era of “probabilistic identity” and “privacy sandboxes ( or whatever they are called today)”. Translated to data-speak: instead of that clear join key that sticks your tables together like teflon, you’re going to be dealing with fuzzy matching, server side proxies, and differential privacy algorithms that add noise to your data on purpose and add AI to that mix as well.
I wrote a primer to make this consumable for anyone needing a quick intro.
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. It’s a great tool to get newer folks updated quickly with the nuances of ecosystem. None of this is really rocket science and well-known to all those immersed in this day-to-day but writing this helped summarize a lot of concepts which in turn helps stay on top of the rapidly changing industry happenings. Hopefully it’s as useful to someone getting their first exposure to this. My goal was to include the topics that resonated most with me and add code snippets that helped form up the concepts.
What you will learn:
- Understand the “alphabet soup” of the ecosystem (DSPs, SSPs, RTB) to effectively translate marketing requirements into technical data solutions.
- Data Mechanics & Measurement: the technical underpinnings of how impressions, clicks, and conversions are tracked, counted, and attributed across massive datasets.
- Boiler plate code for illustrating key technical concepts without needing access to an ad exchange.
This is a working document free for anyone to contribute.
This was a fun exercise to also write a ton of boiler plate code and brainstorm some areas the industry might go to. Being a programmer these days is a bit like a singer using auto-tune. You provide the direction while the code tunes itself – the quality of which is left to judgement.
Resources I highly recommend:
“Programmatic Advertising” by Oliver Busch
Deep dive into programmatic ecosystems. Academic but thorough. Good for understanding the economics and game theory of ad auctions.
“Data-Driven Marketing” by Mark Jeffery
Explains marketing analytics and attribution modeling. Less technical, more strategic. Good for understanding the business side.
“Trustworthy Online Controlled Experiments” by Kohavi, Tang, and Xu
The definitive book on A/B testing. Microsoft research. If you’re running experiments (and you should be), read this.
“Designing Data-Intensive Applications” by Martin Kleppmann
Not AdTech-specific, but essential for understanding the distributed systems that power AdTech platforms. Best technical book I’ve read in years.
AdExchanger -Daily news on programmatic advertising, ad tech M&A, privacy regulations
Merketecture: Anything by Marketecture/Ari Paparo
AdTech God – Lord of RTB, God of Programmatic Prophecy
Digiday -For understanding publisher perspectives and broader industry trends.
MarTech Today / MarTech – Good for staying on top of marketing tech trends beyond just AdTech.
Yield – The full scoop on Google’s advertising dominance. ( I liked this so much I am re-reading it)