You are designing a partitioning strategy for a SQL Server Analysis Services (SSAS) cube. New data is loaded in real-time into the data warehouse that feeds the cube. Between 10 million and 15…

HOLAP stores aggregation data inside the SSAS multidimensional store, giving the same query speed benefit as MOLAP for aggregated queries during business hours. Because detail-level data is served directly from the relational source rather than copied into the cube, rows loaded into the Azure SQL Database fact table are visible to the cube immediately without requiring a partition reprocess, satisfying both performance and freshness requirements.

Weekly partitions are too coarse for a daily load of 10-15 million rows and do not specify a storage mode, so neither the query performance target nor the near-real-time data freshness requirement can be reliably met.

MOLAP copies all data - aggregations and detail rows - into the cube's multidimensional store, so any rows loaded into the data warehouse after the last reprocess are invisible to cube users until a full partition reprocess completes, violating the requirement to surface data as soon as it is loaded.

Identical to choice C, MOLAP mode still requires a full partition reprocess after each data load before newly ingested rows become queryable, which fails the requirement to make data available as soon as possible after it is loaded into the data warehouse.