Question

A data engineer is preparing data in a Fabric lakehouse to feed a star-schema dimensional model. The source is a set of highly normalized operational tables (Customers, Addresses, Regions, and SalesReps) that must be combined into a single wide Customer dimension table with columns from all four source tables. The transformation involves multiple joins and needs to run nightly over hundreds of millions of rows. Which approach best prepares this denormalized dimension for the dimensional model?

Accepted Answer

Create a PySpark notebook that joins the four source tables and writes the denormalized result to a Delta table in the Gold layer

Answer

Create a OneLake shortcut to each of the four source tables so the dimensional model can reference them directly without physical joins

Answer

Use a KQL query in an Eventhouse to aggregate the four tables into a materialized view for the dimension

Answer

Keep the four normalized tables as-is and build the join logic inside the semantic model's relationships at query time