How we built a city-scale event aggregation platform using LLMs for every variable, judgment-intensive task, onboarding, parsing, classification, and enrichment and the architectural pattern that
Explore how we build the data foundations that make AI and machine learning possible. Data engineering is about collecting, cleaning, and organizing raw information so it's reliable and ready for analysis. In this category, you'll find posts on pipelines, storage strategies, and best practices for making data usable at scale. Most come out of client projects where the data turned out to be messier than the model, so they stay practical: what breaks after a pipeline has run daily for a year, and how to tell a data problem from a model problem.
