We’re sold, we’re all about building lake-based data platforms these days – but whether you’re all about the Fabric Lakehouse or Warehouse, whether you’re into custom lakes or OneLake all the way, there are constant arguments about how things should be structured. What if you’ve gone medallion but the zones don't quite fit what they were trying to achieve, and no one in the company understands what "silver" vs. "gold" actually means?

Is the Medallion Architecture right for most businesses - and how should you interpret the advice? What are the different stages of data curation and how do they work in reality? How should we think about schema evolution, data cleansing, record validation, and traditional data modeling techniques, layering them on top of our medallion zones so we truly understand what happens where.

GUEST BIO (Simon Whiteley) 👤
Simon is a Databricks Beacon, Microsoft MVP and owner of Advancing Analytics. He’s been building and designing lakehouses before they were called lakehouses, and spends most of his time championing data engineering and ranting about Spark on Youtube. When not tinkering with tech, you’ll find Simon wandering the streets of London, always hunting for interesting things to eat, drink and do!

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