Heavy Data

OpenAI built their in-house data agent for structured warehouse data, where schema, lineage, and queries come for free. Files in S3, GCS, or Azure - videos, sensor logs, image corpora, PDFs - have none of that, and the problems get a lot more interesting. Here is how we built the four foundations that close the gap.

Neural data like EEG and MRI is never 'finished' - it's meant to be revisited as new ideas and methods emerge. Yet most teams are stuck in a multi-stage ETL nightmare. Here's why the modern data stack fails the brain.

Parquet is great for tables, terrible for images and video. Here's why shoving heavy data into columnar formats is the wrong approach - and what we should build instead. Hint: it's not about the formats, it's about the metadata.

LLMs can finally interpret unstructured video, audio, and documents — but they can't do it alone. This post introduces the concept of heavy data and explores how modern teams build multimodal pipelines to turn it into AI-ready data.