SSAS Quick Reference Guide

If you are looking for a tool to automate maintenance of SQL Server database which can perform deeper and faster analysis of data. Then SSAS can be taken into consideration. This cheat sheet will guide you with the basic concepts which is required to get started with it. It is a handy reference sheet for the beginners and also the one having knowledge about the BI tools.
SSAS Cheat Sheet
Further, if you want to learn AWS in depth, you can refer to the tutorial blog on SSAS.
You can also download the printable PDF of this SSAS cheat sheet
SQL Server Analytical Services (SSAS): SSAS is a technology from Microsoft Business Intelligence stack, which is used to develop online Analytical Processing (OLAP) solutions. It can also be used to create cubes using data from data marts/ data warehouse for faster and efficient data analysis.
Cubes: Cubes are the multi-dimensional data sources which has two basic constituents named dimensions and facts (measures). Dimensions are referred to as Master Tables and facts are referred to as measurable details
Multi-Dimensional expressions (MDX): MDX is a query language that is used to query a cube

Key features of SSAS:

  • Speed: It takes less time to respond to a query due to aggregation of the facts
  • Data Analysis: Allows multi-dimensional analysis facilitated by the cubes
  • Automatic Link and display: It provides the facility to automatically link and display the report
  • Good data model: For better business reporting and analysis a good data model can be created

MDX (Multi-dimensional expressions): It is a query language used for retrieving data from multidimensional databases like OLAP databases
Multi-dimensional database: It is referred to as a cube which is a foundation of multi-dimensional databases and each cube typically contains more than two dimensions
OLAP cube: OLAP cube is a technique which is used to hold the data in an optimized form and used to analyze the data with quick response
Star schema: A schema where every dimension present in the Data Source View (DSV) is directly linked or related to fact or measurable table. It consists of a DE normalized data and can be used in small companies with small databases
Snowflake schema: It is a schema where some dimensions are linked directly to a fact table and some are indirectly linked to fact tables. It consists of a normalized data and can be used in large companies with big databases
Star Flake: It is a hybrid structure which contains a combination or star (DE normalized data) and snowflake (normalized data) schema
Data Source Views (DSV): DSVs enable to create logical view of only the tables involved in the data warehouse design

Key features of SSAS
SSAS Architecture

MDX Expressions:

To calculate Simple Gross Profit[Measures].[Sales Amount] – [Measures].[Total Product Cost]
To calculate sales in a particular country e.g. (Canada)([Measures].[Sales Amount], [Customer].[Country].&[Canada])
To calculate year to date sales of any level of date hierarchyAGGREGATE(
PeriodsToDate( [Date].[Calendar Hierarchy].[Year],
[Date].[Calendar Hierarchy].CurrentMember ),
Alternate year to date expressionAGGREGATE (
YTD ([Date Order].[Calendar].CurrentMember),
Measures.[Sales Amount])
To check the product rankingIIF (Product.Product.CurrentMember IS Product.Product.[All],NULL,
IIF (Measures.[Sales Amount] = 0, NULL,
RANK(Product.Product.CurrentMember, ORDER (Product.Product.Members, Measures.[Sales Amount]))))
Sales from 365 days ago(ParallelPeriod([Invoice Date].[Date Hierarchy].[Date], 365, [Invoice Date].[Date
Hierarchy].CurrentMember), [Measures].[Sales Amount])
Sales done in the previous period(Measures.[Sales Amount],
[Date Order].[Calendar].CurrentMember.PrevMember)
To view top 10 selling productsTopCount
(Product.Product.Product.Members,10,Measures.[Sales Amount])
To display the average sales from three years till dateAvg(
{ParallelPeriod( [Date].[Date].[Year], 3,
StrToMember(“[Date].[Date].&[” + Format(now(), “yyyyMMdd”) + “]”)) :
StrToMember(“[Date].[Date].&[” + Format(now(), “yyyyMMdd”) + “]”)} , [Measures].[Sales Count])
Drillthrough Action caption‘Get Sales Details for’
To change SSAS Calculation text colorIIF([Measures].[Profit Percentage] < .40, 255 , 0)
Changing a calculation using scope statementSCOPE ([Measures].[Scope Profit]);
THIS = ([Measures].[Sales Amount] – [Measures].[Standard Product Cost]);
Clear ration value when at all levelsSCOPE ([Customer].[Customer Geography].[All],
SSAS KPI Value Expression[Measures].[Sales Amount] * 1.2
SSAS KPI Goal ExpressionCase
When IsEmpty
([Date Order].[Fiscal].[Fiscal Year],
1,[Date Order].[Fiscal].CurrentMember))
Then [Measures].[Sales Amount]
Else 1.10 *
([Measures].[Sales Amount],
ParallelPeriod([Date Order].[Fiscal].[Fiscal Year],
1,[Date Order].[Fiscal].CurrentMember))
SSAS KPI Status ExpressionCase
When KpiValue( “Sales Revenue YTD” ) / KpiGoal( “Sales RevenueYTD” ) > 1
Then 1
When KpiValue( “Sales Revenue YTD” ) / KpiGoal( “Sales Revenue YTD” ) <= 1
KpiValue( “Sales Revenue YTD” ) / KpiGoal( “Sales Revenue YTD” ) >= .85
Then 0
Else -1
SSAS KPI Trend ExpressionCase
When IsEmpty
([Date Order].[Fiscal].[Fiscal Year],
1,[Date Order].[Fiscal].CurrentMember))
Then 0
When VBA!Abs
((KpiValue( “Sales Revenue YTD” )- (KpiValue( “Sales Revenue YTD” ),
( [Date Order].[Fiscal].[Fiscal Year],
1, [Date Order].[Fiscal].CurrentMember)))<=.02
(KpiValue( “Sales Revenue YTD” ),
( [Date Order].[Fiscal].[Fiscal Year],
1,[Date Order].[Fiscal].CurrentMember))) <=.02
Then 0
When (KpiValue( “Sales Revenue YTD” )- (KpiValue( “Sales Revenue YTD” ),
( [Date Order].[Fiscal].[Fiscal Year], 1,
[Date Order].[Fiscal].CurrentMember)))
/(KpiValue( “Sales Revenue YTD” ),
ParallelPeriod( [Date Order].[Fiscal].[Fiscal Year],1,[Date Order].[Fiscal].CurrentMember)) >.02
Then 1
Else -1

Download a Printable PDF of this Cheat Sheet

With this, we come to an end of AWS Cheat sheet. To get in-depth knowledge, check out our interactive, live-online SSAS certification Training here, that comes with 24*7 support to guide you throughout your learning period. Intellipaat’s SSAS online training equips you with various analysis techniques, discovery trends and building OLAP cubes for generating business insights.

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