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Chapter 2 - Video # 9

Measures, Dimensions, and Cubes

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Chapter 2: Business Intelligence & Data Warehousing with SSASCourse: SQL Server 2008/R2 Analysis ServicesCourse Id: 165Presented by Scott Whigham

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• An MDB is comprised of one or more cubes– Subsets of the database

– Granular at the specific business function/topic

Measures, Dimensions & Cubes

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• The numbers on the face of the cube (called a “cell”) are the measures– Measures are numbers are stored in the MDB in

pre-calculated aggregate form

Measures, Dimensions & Cubes

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• Common measures:– UnitsSold

– SalesAmount

– TaxAmount

– DiscountAmount

– NumberOfDefects

– NumberOfCustomers

– AverageUnitPrice

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• Measures are numbers that your analysts/execs/managers want to know– Measures are answers

• “How many sales did we do?”

• “How many defects have we had?”

• “How much in taxes did we pay?”

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• Dimensions are the context for your measures– Measures are answers

– Dimensions are how you want to view the answer

• “How many sales did we do over each of the past five years?”

• “How many defects have we had for each machine?”

• “How much in taxes did we pay for each salesperson?”

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• Our cube analogy is a great visual for understanding the relationship dimensions and measures have

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• Dimensions can contain hierarchies– A “Time” dimension can roll up from “Day” all the

way to “Year”

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• A Geography dimension might look like this:

• Country, State/Province, City, Postal Code

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• We’ll cover dimensional design in detail in chapters 4 and 5

• For now: back to the cube!

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• A cube is a part of a multidimensional database– It is not the multidimensional database; it is just a

subset of the database

– A cube contains measures and dimensions

– The data stored in a cube is pre-calculated aggregates at various levels of dimensions and their hierarchies

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• “Scott – you keep talking about pre-calculated aggregates over and over… Get to the point!”

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• Okay – so back to our discussion of relational databases– “Groannnnnnnnnnnnnnnn….”

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• Because the cube contains pre-calculated aggregates, theoretically drilldown and drillthrough are extremely fast– It is simply a matter of “pivoting the cube to the

right face”

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• Reports on relational data can perform drilldown and drillthrough but must wade through potentially millions of records with each drill-action!

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• “Wait – did you just imply that I don’t need an MDB; that I can do everything that I can with an MDB with my relational database?”– Why yes, I did!

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• The whole point of an MDB is to make it:– Easier to write analytical queries

– Faster to get the data back

• MDBs are not a replacement for relational databases– MDBs simply do analytics better!

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• To sum up:– MDBs consist of cubes

– Cubes are made up of measures and dimensions

– Measures are the numbers

– Dimensions are the context for the numbers

– Chapters 4 and 5 will continue this discussion

– Pierluigi Collina is a man of many faces

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• What Is Data Mining?

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