Dimensional Design Tips

Koen Verbeeck provides some helpful hints when designing dimensions in SQL Server Analysis Services Multidimensional models:

Although traditional dimension modeling – as explained by Ralph Kimball – tries to avoid snowflaking, it might help the processing of larger dimensions. For example, suppose you have a large customer dimension with over 10 million members. One attribute is the customer country. Realistically, there should only be a bit over 200 countries, maximum. When SSAS processes the dimension, it sends SELECT DISTINCT commands to SQL Server. Such a query on top of a large dimension might take some time. However, if you would snowflake (aka normalize) the country attribute into another dimension, the SELECT DISTINCT will run much faster. Here, you need to trade-off performance against the simplicity of your design.

There are several good tips here.

Related Posts

Automatic Processing Of Azure Analysis Services Models

Dustin Ryan shows how to use Azure Functions to refresh Azure Analysis Services models: Download the latest client libraries for Analysis Services. This needs to be done on your local machine so you can then copy these files to your Azure Function App. After you’ve downloaded the client libraries, the DLLs can be found in […]

Read More

Re-Shaping Data Flows

Maneesh Varshney explains some methods to trim the fat out of analytical data flows: Big data comes in a variety of shapes. The Extract-Transform-Load (ETL) workflows are more or less stripe-shaped (left panel in the figure above) and produce an output of a similar size to the input. Reporting workflows are funnel-shaped (middle panel in […]

Read More

Categories

June 2017
MTWTFSS
« May  
 1234
567891011
12131415161718
19202122232425
2627282930