Analysis Services tabular models can store data in a highly-compressed, in-memory cache for optimized query performance. This provides fast user interactivity over large data sets.
Large data sets normally require table partitioning to accelerate and optimize the data-load process. Partitioning enables incremental loads, increases parallelization, and reduces memory consumption. The Tabular Object Model (TOM) serves as an API to create and manage partitions. TOM was released with SQL Server 2016 and is discussed here. Model Compatibility Level 1200 is required.
- Is intended to be generic and configuration driven.
- Works for both Azure Analysis Services and SQL Server Analysis Services tabular models.
- Can be leveraged in many ways including from an SSIS script task, Azure Functions and others.
Thanks to Marco Russo (SQLBI) and Bill Anton (Opifex Solutions) for their contributions to the whitepaper and code sample.