NET, and they all come with their pros and cons. There are many techniques to dynamically partitioning tables in SSAS Tabular data models such as using TMSL scripting, SSIS, Powershell. So before jumping into the how-to-do part let me answer the why SQL question. So disregarding whether our data are on-prem or in the cloud, the storage and the computation needed to run our BI system comes with a cost.Īs it is common to keep 5+ years of historic data in a BI production system this is certainly not a good idea to keep historical data on a Development or a Test server.Īllocating the same computational power and storage capacity for all our servers (Dev, Test, UAT, Prod) is a waste of money and energy. Reducing the cost of our On-prem or cloud infrastructure.Reducing the CPU and memory consumption.Reducing the time processing of our model.Putting things in another words limiting the volume of data means: In this post, I’m going to show how we can simply dynamically partitioning tables in SSAS using SQL. Parallel processing and incremental loading are common scenarios for using the partitions, however, another scenario where partitions are also very useful is for reducing the volume of data across each environment. Set different data refresh frequency, historical data don’t need to be refreshed every day.Reduce the volume of data across different environments.Each partition contains a portion of the data, partitions can be processed in parallel independent of other partitions or excluded from processing operations if they don’t need to be refreshed. Partitions split a table into logical partition objects. Generate dynamic filters for each partition.How can we keep control over the volume of data across our different servers?.
Why should we limit the volume of data?.
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