[seektable]

> everything is translated to raw sql then pushed to the database layer

All ROLAP-kind of BI tools do that (including PowerBI when it uses direct-query connection mode), it is expected that underlying data sources are fast enough to handle these aggregate queries very quickly. In fact this approach may be used even with non-OLAP databases (like PostgreSql or SQLServer) and specialized analytical datastore is needed only for really big datasets (BigQuery, Snowflake, ClickHouse etc). In many cases correct usage of report parameters that can filter DB records by indexed columns OR usage of pre-aggregated materialized views, or tuning of SQL query generation (say, avoid JOINs and SQL-calculations when they are not needed for the concrete report) can solve performance issues.

This doesn't mean that Excel's PivotTable (and SSAS cubes) is good and ROLAP-kind pivot tables are bad because their applications are different. In cases when pivot tables should show actual (near real-time) data and this is main purpose of this kind of reports in BI tools; when users need to explore some dataset in a disconnected mode they always may export concrete report's data to Excel - in fact, some BI tools can export their internal pivot table into Excel file with pre-configured PivotTable.