So you did everything correctly with your OLAP project.You had a trained Project Manager leading the team; users were directly involved from the beginning and you had clear definitions of the scope of the project and ultimately all the various reporting needs and objectives.In OLAP cubes, data (measures) are categorized by dimensions.OLAP cubes are often pre-summarized across dimensions to drastically improve query time over relational databases.An OLAP cube is a multidimensional database that is optimized for data warehouse and online analytical processing (OLAP) applications.An OLAP cube is a method of storing data in a multidimensional form, generally for reporting purposes.Kevin has worked in many lines of business, including insurance, manufacturing, health care, consumer packaged goods, accounting and finance, advertising, and many others.
For our examples, we will discuss making changes to a dimension attribute field that is also the key field.
Up until SQL Server 2012, the multidimensional cube was the only Online Analytical Processing (OLAP) offering within the Microsoft BI stack (I discussed the Tabular model and its 2016 enhancements in Part 1 of this blog series).
As such, Microsoft has released multiple iterations of the multidimensional cube to develop it into a mature BI product.
I recently attended Microsoft’s quarterly Minnesota BI User Group Meeting, and I particularly enjoyed learning from Will Weber (a Data Platform Solution Architect at Microsoft) about their upcoming release of SQL Server 2016.
This is the second installment in a blog series that will summarize the various additions and enhancements that Microsoft is introducing to their BI stack from the perspective of a BI developer. Microsoft SQL Server Analysis Services (SSAS) has been around for almost 20 years.