Summary

As lakehouse implementations grow in popularity, data warehouses still play a key role in data-driven organizations. Many of the patterns that were explored in the prior lakehouse chapter can also be applied to a data warehouse. The major difference comes in the developer skillset. Lakehouses are Spark-centric, while data warehouses are T-SQL-centric. For the foreseeable future, these two items will live and work in tandem. Fabric provides a seamless experience to combine lakehouses and warehouses through OneCopy, cross-database querying, and standardizing on the Delta format.

In this chapter, you learned how to build out an end-to-end data warehouse analytics system. You learned how to create a data warehouse and a code-first and no-code approach for loading warehouse tables, how to transform data using T-SQL, and how to curate a data model that can then be used in a Power BI report. These are by no means the full extent of the data warehouse functionality but instead represent...