Automated Partition Management with Azure Analysis Services

added by DotNetKicks
1/19/2017 5:09:22 PM

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Azure 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 datasets normally require table partitioning to accelerate and optimize the data-load process. Partitioning enables incremental loads, increases parallelization, and reduces memory consumption.