The data model of a data warehouse is most commonly relational, because SQL is generally a good fit for analytical queries. There are many graphical data analysis tools that generate SQL queries, visualize the results, and allow analysts to explore the data (through operations such as drill-down and slicing and dicing).
On the surface, a data warehouse and a relational OLTP database look similar, because they both have a SQL query interface. However, the internals of the systems can look quite different, because they are optimized for very different query patterns. Many database vendors now focus on supporting either transaction processing or analytics workloads, but not both.
Some databases, such as Microsoft SQL Server, SAP HANA, and SingleStore, have support for transaction processing and data warehousing in the same product. However, these hybrid transactional and analytical processing (HTAP) databases (introduced in “Data Warehousing”) are increasingly becoming two separate storage and query engines, which happen to be accessible through a common SQL interface.