Exploring Lakehouse//RT and Reyden: Can Databricks Handle FHIR Data at Scale?
by Nick Passero, Director AI Data & Analytics, Databricks Practice Lead and Balu Muthiah, Sr. Solutions Architect, AI Data Platforms
At DAIS 2026, Databricks announced Lakehouse//RT, powered by Reyden, a ground-up engine rewrite and not an update to Photon. It runs against existing Delta and Iceberg tables without restructuring, requires Unity Catalog, and keeps data in open formats.
Databricks frames the value proposition around three costs of maintaining a separate serving layer: data duplication into proprietary storage, governance policies that don’t travel with the data out of Unity Catalog, and the ongoing engineering burden of owning a second pipeline.
Official performance claims include up to 16x better performance versus real-time serving layers, with response times as low as 10 milliseconds on smaller datasets and sub-100 milliseconds on larger ones.
How We’re Using It
A healthcare client came to us with a straightforward problem: their FHIR data was already in...
Copyright of this story solely belongs to perficient.com. To see the full text click HERE
