It’s easier than ever to build analytics products and a data-driven culture. With cloud-native databases and modern data engineering platforms, we have the performance and scale to build limitless solutions without heavy lifting. This raises the question: should we use the same modeling techniques that emerged from data warehouses constrained by compute and storage?
In this session, I’ll contend that traditional modeling strategies evolved in response to technical limitations that don’t exist today in the data cloud. I’ll identify a few key architectural concepts that should exist in your modern data stack, regardless of whether you’re building a data lake, warehouse, or lakehouse.