BIAnalytix Tip Summary: Cube Partioning

One of the biggest challenges in OLAP solutions is to provide a solution that allows you to process your data quickly and also provide excellent query performance. When data sizes become exceedingly large, partitioning measure groups can be an effective and efficient way of improving query performance and processing time. For the media industry, this can be especially true, where data sizes are often in the billions of rows of data. Finding the appropriate number of partitions can require some thought. Too many partitions can hurt performance as badly or worse than too few partitions. The Microsoft recommendation is to not exceed 20 Million Rows of data per partition and 2,000 partitions per measure group.

From a cube processing standpoint in MOLAP mode, the cube engine can really benefit from fact partitioning. By default SSAS optimizes processing time by processing objects in parallel. By partitioning a large measure group, multiple partitions can be processed at the same time. Moreover, when SQL Server partitioning is combined with SSAS cube partitioning, build-time and performance can be improved. This is achieved by increasing the parallelism in both the SSAS engine and the SQL server database engine. Another key benefit of partitioning your data is that it allows the cube engine to locate data quicker by scanning smaller files of data in parallel to answer a query more efficiently. Cube partitions should be chosen by common querying splits like years, quarters, or months, but should also be designed in a way that doesn’t force most queries to hit multiple partitions to provide a result.

BIAnalytix is an enterprise class BI system that utilizes features such as cube partitioning to provide high performance BI Solutions.

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