IMS 6217: Data Warehousing / Business Intelligence Part 3 1 Dr. Lawrence West, Management Dept., University of Central Florida [email protected] Analysis

IMS 6217: Data Warehousing / Business Intelligence Part 3

1Dr. Lawrence West, Management Dept., University of Central [email protected]

Analysis Services

• Analysis Services vs. the Data Warehouse vs. OLTP DB

– Precalculated Aggregates

– Hierarchical Awareness

• Creating the OLAP DB

– The Data Source View

– The Cube

• Fact & Dimension Tables

• The Time Dimension

• Measures

• Using the Cube



Analysis Services—Why we Care

• Three levels of data sources for analysis

– All three are separate copies of the data

– Each has advantages/disadvantages & purposes

• Source data

– OLTP relational databases

– Other source data including external

• Data Warehouse

– Source data integrated

– Still a relational database

• Analysis Services (OLAP) Database



Analysis Services—Why we Care (cont.)

• Data Warehouse Strengths

– Integrated data

– 'Scrubbed' data

– Shortened relationship paths→Simpler queries

– Optimized for queries rather than throughput

• Data Warehouse Limitations

– Still a relational database

– Performance lags when querying and summing millions of records



The OLAP / Analysis Services Approach

• In their simplest forms OLAP databases have a logical structure similar to the star or snowflake schema we saw in the DW

– Fact tables

– Dimension tables

• Data storage structure is wildly different from relational DBMS

• Fact/Dimension tables are stored in 'cubes'

– Multi-dimensional (not just three) relationships between fact and dimension tables

– Preprocessed aggregates stored in DB



The OLAP / Analysis Services Approach (cont.)

• Recall that fact tables contain

– Keys that indicate dimensionality of the data

– Measures that contain values of interest

• We will design Cubes based arounda single fact table

– Other approaches acceptable including multi-fact table cubes

SALES

TimeKeyOrderedTimeKeyShippedTimeKeyPmntRcvdProductKeyCategoryKeyCustomerKeySalesTerrKeySalesRepKeyUnitsSoldSalesPriceValueSoldTotalDiscounts



The OLAP / Analysis Services Approach (cont.)

• The OLAP engine is aware of

– The relationship between the values in the dimensional key columns and the measures in the fact table

• Every sale is for one

– Time Key

– Customer Key

– etc

– Hierarchies in dimensional tables

• Country→State →City

• Year →Month →Date



Precalculated Aggregates

• The OLAP Engine precalculates aggregates along dimensions in the fact table

• If querying total sales value by customer andproduct this value may already be stored for each combination of customer and product

– Aggregates are calculated and stored during processing of the cube (later)



Intelligent Hierarchies

• OLAP intelligently uses precalculated aggregates to total on hierarchies

• If aggregates are already calculated for sales by product by customer…

• … sales by product by country use the precalculated aggregate rather than querying the detail data

• There are special tools for establishing hierarchical relationships among time dimension components

• Relationships in snowflake schema will be automatically detected

• Others can be established at design time



Creating the OLAP DB

• Create the OLAP DB from the DW– They can also be created directly from source data

• Use Business Intelligence Development Studio to design, create, and load the OLAP DB– The Visual Studio project contains the definitions

needed to design, create, and load– The project also creates the Analysis Services DB

• SQL Server & Analysis Services must both be running



Creating the OLAP DB (cont.)

• Steps

– Create Analysis Services Project with connection(s)

– Create Data Source View to define data to be loaded

– Generate OLAP DB

– Load OLAP DB

• OLAP DB available for use

– Direct browsing

– Serving via Analysis Services server

– Reporting Services

– Excel



Creating the OLAP DB

• NewBusinessIntelligenceProject

• Type isAnalysisServices

• Manageproject filelocations



Create Data Source(s) for the Project

• Create a data source just as we didfor the data warehouse load project

• Point the data source to the data warehouse DB

– Create new connection if necessary

– Select "Default" Impersonation Information if DW DB does not require login



Create the Data Source View

• The Data Source View (DSV) is a map from the source data (data warehouse in our case) to the OLAP DB

– May include data transformations

– Allows fact data and dimensions to be identified

– Allows hierarchies to be established

• Special tools for time hierarchies

• Create new DSV in Solution Explorer

– Set Data Source



Create Data Source View (cont.)

• Select fact tableto be loaded

• Select dimension tables

– Use Add RelatedTables button

– Manually select

– Include hierarchicaltables as necessaryfrom snowflakeschema

• Name DSV when all tables are selected



Create Data Source View (cont.)

• DSV template iscreated from theselected tables

• Template may bemodified

– Add calculatedcolumns

• Ready to add newcube when DSVis complete



Create Cube

• Create new cube from Solution Explorer

• Select Build cube from data source

• Select the DSVthat was created tobe the basis for thenew cube



Build Cube—Confirm Fact & Dimension Tables

• Confirm suggestedfact and dimensiontables

• Wizard frequentlymisidentifies dimension tablesas fact tables

– Just check

• Be sure to identifythe Time DimensionTable



Build Cube—Map Time Dimension Columns

• Time has built in hierarchies

• Map the Time Dimcolumns to thepredefined timehierarchical concepts

– Not all will bemapped



Build Cube—Identify Measures

• Uncheck spuriouscolumns that willnot be used as measures in thefact table

• Next step detectshierarchies

– No operatorchoices



Build Cube—Review Hierarchies

• The hierarchiesscreen allows you toreview, delete, &modify hierarchies



Building the Cube—Name the Cube

• Give the cube ameaningful name

• Default is the sameas the DSV which should probably notbe used

• Click Finish to build the cube design



Building the Cube—Reviewing Cube Design



Building the Cube—Reviewing Cube Design

• The cube structure tab will show the cube design in a way that looks much like the DSV

• Any changes will be reflected

– Calculated columns

– Renamed columns



Processing the Cube

• The cube must be processed before it can be used– Select Process… from the Cube menu– On first run you will be prompted to build and deploy

the project first

– Select Run from the Process Cube dialog– This may take some time—this is where the data is

being loaded into the OLAP DB and initial aggregations created



Demonstrations

• Browser

• Reporting Services

• Excel Access to OLAP DB

• Do Your Own

– Build a Cube around the Adventure Works DW Internet Sales Fact Table

Documents

IMS 6217: Data Warehousing / Business Intelligence Part 3 1 Dr. Lawrence West, Management Dept., University of Central Florida [email protected] Analysis