ISQS 3358, Business Intelligence Cubism – Measures and Dimensions Zhangxi Lin Texas Tech...

ISQS 3358, Business IntelligenceISQS 3358, Business Intelligence

Cubism – Measures and Cubism – Measures and DimensionsDimensionsZhangxi Lin

Texas Tech University

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OutlineOutlineWhere we’ve beenPopulating fact tableCreating a cube with SSISMeasuresTypes of dimensions Cube design tabs

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Structure and Components Structure and Components of Business Intelligenceof Business Intelligence

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SSMSSSMS SSISSSIS SSASSSAS

SSRSSSRS

SASEM

SASEM

SASEG

SASEG

Snowflake Schema of the Data Snowflake Schema of the Data MartMart

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Manufacturingfact

DimProduct

DimProductSubType

DimProductType

DimBatch

DimMachine

DimMachineType

DimMaterial

DimPlant

DimCountry

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Where we’ve been and where we Where we’ve been and where we are noware now Exercise 1: Getting started Exercise 2: Creating data marts Exercise 3: Creating a cube from a data mart Exercise 4: Populating dimensions of a data

mart Exercise 5: Exploring features of ETL data

conversion tasks Exercise 6: Loading fact tables

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What we need to do with the half-What we need to do with the half-done data mart?done data mart?Populate DimBatch dimenstion tablePopulate ManufacturingFact tableBuild an OLAP cube (we already did

this before)Check measuresCheck dimensions

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LOADING FACT LOADING FACT TABLESTABLES

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Exercise 6: Loading Fact Exercise 6: Loading Fact TablesTablesProject name: MMMFactLoad-lastnamePackage name: FactLoad.dtsxTasks

◦ Create Inventory Fact table◦ Load Dim Batch◦ Load Manufacturing Fact◦ Load Inventory Fact

Deliverable: email a screenshot of the “green” outcome of the ETL project to [email protected]

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Inventory Fact TableInventory Fact Table

Create a Table InventoryFact in database MaxMinManufacturingDM-lastname.◦ Compound primary key: DateOfInventory,

ProductCode, and Material◦ Define two foreign keys

Column Name Data Type Allow Nulls

InventoryLevel Int No

NumberOnBackorder Int No

DateOfInventory Datatime No

ProductCode Int No

Material Varchar(30) No

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Data Sources for Loading Data Sources for Loading FactFactFor loading DimBatch table and

ManufacturingFact table◦ BatchInfo.CSV

For loading InventortyFact table ◦ OREDB.OrderProcessingSystem.Inventory

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Control Flow for Loading Facts and Control Flow for Loading Facts and the Remaining Dimensionthe Remaining Dimension

Note: to ease debugging, you may use three packages and test them one by one, instead of doing everything in one package

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Flat File Connection Flat File Connection Data types

◦BatchNumber, MachinNumber: four-byte signed integer [DT_I4]

◦ProductCode, NumberProduced, NumberRejected: four-byte signed integer [DT_I4]

◦TimeStarted, TimeStopped: database timestamp [DT_DBTimeStamp]

Only check BatchNumber as the input of Dim Batch

All columns are needed for fact tables

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Load DimBatch Data FlowLoad DimBatch Data Flow

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Load DimBatch Data FlowLoad DimBatch Data Flow

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Note: Because of duplication in the source file, we may insert An Aggregate item after the Flat File Source item.

The Flat File SourceThe Flat File Source

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Sort Transformation

In the Aggregate item,Define “Group-by” BatchNumber.

In Derived column item, Define BatchName From BatchNumber

Load Fact Data FlowLoad Fact Data Flow

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Derived Columns for the Fact Derived Columns for the Fact tabletable

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Expressions for the Derived Expressions for the Derived ColumnsColumnsAcceptedProducts

◦ [NumberProduced] – [NumberRejected]ElapsedTimeForManufacture

◦ DATEDIFF(“mi”, [TimeStarted],[TimeStopped])

DateOfManufacture◦ (DT_DBTIMESTAMP)SUBSTRING((DT_WSTR,

25)[TimeStarted],1,10) This expression converts TimeStarted into a

string and selects the first ten characters of that string. This string is then converted back into a date time, without the time portion.

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OLE DB DestinationFor loading the facttable

Load Inventory FactLoad Inventory FactOLE DB Source

◦ OrderProcessingSystem.InventoryFactOLE DB Destination

◦ MaxMinManufacturingDM-lastname.InventoryFactNo transformation

There are two ways to loading the table◦ Create the table and use ETL to load it◦ Import directly from the source to the database

MaxMinManufacturingDM-lastname

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Debugging ResultsDebugging Results

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Loading DimBatch Loading ManufacturingFact

BUILDING AN OLAP BUILDING AN OLAP CUBECUBE

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Three Steps to Create a Cube from Three Steps to Create a Cube from Data SourcesData SourcesDefining data sourceDefining data source view

◦ Add in three new columns of year, quarter, and month for the two fact tables

Building a cube. ◦ Define a new dimension Dim Time from

Manufacturing Fact tableCustomize the cube:

◦ Link two fact tables in a cube◦ Define new primary key for Dim Time◦ Define calculated measures◦ Relate dimensions to measures

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T-SQL Expressions for DS View T-SQL Expressions for DS View Definition - ManufactureDefinition - Manufacture YearOfManufacture

CONVERT(char(4),YEAR(DateOfManufacture)) QuarterOfManufacture

CONVERT(char(4), YEAR(DateOfManufacture)) + CASE WHEN MONTH (DateOfManufacture) BETWEEN 1 AND 3

THEN 'Q1' WHEN MONTH (DateOfManufacture) BETWEEN 4 AND 6

THEN 'Q2' WHEN MONTH (DateOfManufacture) BETWEEN 7 AND 9

THEN 'Q3'ELSE 'Q4'END

MonthOfManufactureCONVERT(char(4), YEAR(DateOfManufacture)) +

RIGHT('0'+CONVERT(varchar(2), MONTH(DateOfManufacture)),2)

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T-SQL Expressions for DS View T-SQL Expressions for DS View Definition - InventoryDefinition - Inventory YearOfInventory

CONVERT(char(4),YEAR(DateOfInventory)) QuarterOfInventory

CONVERT(char(4), YEAR(DateOfInventory)) + CASE WHEN MONTH (DateOfInventory) BETWEEN 1 AND 3

THEN 'Q1' WHEN MONTH (DateOfInventory) BETWEEN 4 AND 6

THEN 'Q2' WHEN MONTH (DateOfInventory) BETWEEN 7 AND 9

THEN 'Q3'ELSE 'Q4'END

MonthOfInventoryCONVERT(char(4), YEAR(DateOfInventory)) +

RIGHT('0'+CONVERT(varchar(2), MONTH(DateOfInventory)),2)

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Data Source ViewData Source View

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New columns

Select Measures PageSelect Measures Page

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Uncheck ManufactureFact Count

Review New Dimensions Review New Dimensions PagePage

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Rename ManufacturingFact to Dim Time

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The finished cube

New dimensionCreated from the Fact table

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CubeStructure

MEASURES MEASURES

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FactsFactsMeasurements associated with a specific

business process.Types of measures

◦Most facts are additive (calculative), such as sum; others are semi-additive (those that can be added along some dimensions, not along others), non-additive (such as max, average), or descriptive (e.g. factless fact table).

Many facts can be derived from other facts. So, non-additive facts can be avoided by calculating it from additive facts.

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Calculated measuresCalculated measuresThe definition of calculated measure is stored

in the OLAP cube itself. The actual values that result from a calculated measure are not calculated, however, until a query containing that calculated measure is executed. The results of that calculation are then cached in the cube. The cached value is then delivered to any subsequent users requesting the same calculation.

The expressions of calculation are created using a language known as Multidimensional Expression Language (MDX) script. MDX is different from T-SQL. It is a special language with features designed to handle the advanced mathematics and formulas required by OLAP analysis. This is not found in T-SQL.

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Define Format String “#, #” for measures:

AcceptedProduct, RejectedProject

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Defining a format string

Define Calculated Define Calculated MeasuresMeasures

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DIMENSIONSDIMENSIONS

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Managing DimensionsManaging Dimensions

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Managing DimensionsManaging Dimensions

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Relating Dimensions to Measure Relating Dimensions to Measure GroupsGroups

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Completed Dimension Completed Dimension DefinitionsDefinitions

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Types of DimensionsTypes of Dimensions Fact dimensions: the Dimensions created from attributes in a fact

table Parent-Child dimensions: Built on a table containing a self-

referential relationship, such as a parent attribute. Role playing dimensions: related to the same measure group

multiple times; each relationship represents a different role the dimension play; for example, time dimension plays three different roles: date of sale, data of shipment, and date of payment

Reference dimensions: Not related directly to the measure group but to another regular dimension which in turn related to the measure group

Data mining dimensions: the information discovered by data mining

Many-to-many dimensions: e.g. multiple ship to addresses Slowly changing dimensions

◦ Type 1 SCD – no track◦ Type 2 SCD – tracking the entire history, adding four attributes: SCD

Original ID, SCD Start Date, SCD End Date, SCD Status◦ Type 3 SCD – Similar to Type 2 SCD but only track current state and the

original state; two additional attribute: SCD Start Date, SCD Initial Value

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CUBE DESIGN TABS CUBE DESIGN TABS

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Understanding the Cube Designer Understanding the Cube Designer Tabs Tabs

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Cube Structure: Use this tab to modify the architecture of a cube. Dimension Usage: Use this tab to define the relationships

between dimensions and measure groups, and the granularity of each dimension within each measure group.

Calculations: Use this tab to examine calculations that are defined for the cube, to define new calculations for the whole cube or for a subcube, to reorder existing calculations, and to debug calculations step by step by using breakpoints.

KPIs: Use this tab to create, edit, and modify the Key Performance Indicators (KPIs) in a cube.

Actions: Use this tab to create or modify drillthrough, reporting, and other actions for the selected cube..

Partitions: Use this tab to create and manage the partitions for a cube. Partitions let you store sections of a cube in different locations with different properties, such as aggregation definitions.

Perspectives: Use this tab to create and manage the perspectives in a cube. A perspective is a defined subset of a cube, and is used to reduce the perceived complexity of a cube to the business user.

Translations: Use this tab to create and manage translated names for cube objects, such as month or product names.

Browser: Use this tab to view data in the cube.

ISQS 6339, Data Mgmt & Business Intelligence

Key Performance Indicators Key Performance Indicators (KPIs)(KPIs)

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Digital dashboardCreating a KPI


The MDX expression for KPI The MDX expression for KPI Status Status Expression Expression (MaxMinManufacturingDM)(MaxMinManufacturingDM)

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Case When ROUND([Measures].[percent Rejected],4) < 0.0103

Then 1When ROUND([Measures].[percent Rejected],4) >= 0.0103 AND

ROUND([Measures].[percent Rejected],4) >= 0.0104 Then .5

When ROUND([Measures].[percent Rejected],4) >= 0.0104 AND ROUND([Measures].[percent Rejected],4) >= 0.0105 Then 0

When ROUND([Measures].[percent Rejected],4) >= 0.0105 AND ROUND([Measures].[percent Rejected],4) >= 0.0106 Then -.5

Else -1End


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Calculated measure

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KPI definition anddeployment

KPI BrowserKPI Browser

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Browser View


ActionsActions

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Instructions stored inside the cubeAllow the OLAP cubes to “reach out and

touch someone.”Enable us to define commands,

statements, and directives that are to be executed outside of the cube

Linked to certain objects in the cube, which can be enacted as a menu when a user is browsing the objects. The user can select one of the these actions to accomplish certain tasks.


Types of ActionsTypes of Actions

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Action◦ Dataset◦ Proprietary◦ Rowset - Retrieve a rowset.◦ Statement◦ URL

Drillthrough Action. Defines a dataset to be returned as a drillthrough to a more detailed level

Report Action. Launch a SQL Server 2005 Reporting Services report


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Defining Actions

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Perspectives

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Translations

Q & AQ & A Conceptual level

◦ What are rationale behind the structure of “Data Source”, “Data Source View” and “Cube”?

◦ Why time dimension is so important in a data mart?◦ Why is the multi-levels of dimensions, such as Material-MachineType-

Machine in MaxMinManufacturingDM, useful? ◦ Why do you need to change the primary key of DimTime after it was

created from the MaxMinManufacturingFact table?◦ Can you summarize a number of main differences between a regular

database design and a data mart design? Technical level

◦ After you made changes in a data source node why do you have to check “Mapping” in the data destination node again?

◦ When there is a red wave line under an object, such as a table during cube design, what does it imply? How to solve it? Specifically, when a fact table has such a problem how could it be fixed?

◦ Why not all dimensions appear in the cube structure diagram? ◦ What is the difference between the variable names in the format of

Name and [Name]? ◦ Do you understand the parameters configured in the data flow tasks,

such as those in data sources, data destination, Aggregate node, Derived Column node, etc?

Any other questions?

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Data Mart Application Development Data Mart Application Development DebuggingDebugging Problem 0: You cannot find your database entry. Problem 1: The source node is red after running a

data flow task◦ Causes?

Problem 2: The destination node is red after running a data flow task◦ Causes?

Problem 3: Even though you redefined the source node, the problem remains.

Open problems◦ What are frequently encountered problems in ETL

application implementation?◦ What are the problems you encountered in building a

cube?

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ISQS 3358, Business Intelligence Cubism – Measures and Dimensions Zhangxi Lin Texas Tech...

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