ISQS 3358, Business Intelligence

Cubism – Measures and Dimensions

Zhangxi Lin Texas Tech University 1

Outline

      Where we’ve been Populating fact table Creating a cube with SSIS Measures Types of dimensions Cube design tabs 2

Structure and Components of Business Intelligence

SSMS SSIS SSAS SSRS SAS EG SAS EM 3

Snowflake Schema of the Data Mart

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Manufacturingfact DimBatch DimMachine

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DimProduct

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DimMachineType

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DimProductSubType

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DimProductType

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DimMaterial

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DimPlant DimCountry

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Where we’ve been and where we are 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-done data mart?

     Populate DimBatch dimenstion table Populate ManufacturingFact table Build an OLAP cube (we already did this before) Check measures Check dimensions 6

LOADING FACT TABLES

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Exercise 6: Loading Fact Tables

    Project name: MMMFactLoad-lastname Package name: FactLoad.dtsx

◦ ◦ Tasks ◦ 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 Table

 Create a Table InventoryFact in database MaxMinManufacturingDM-lastname.

◦ Compound primary key: DateOfInventory, ProductCode, and Material ◦ Define two foreign keys

Column Name

InventoryLevel NumberOnBackorder DateOfInventory ProductCode Material

Data Type

Int Int Datatime Int Varchar(30)

Allow Nulls

No No No No No 9

Data Sources for Loading Fact

  For loading DimBatch table and ManufacturingFact table ◦ BatchInfo.CSV

For loading InventortyFact table ◦ OREDB.OrderProcessingSystem.Inventory 10

Control Flow for Loading Facts and the Remaining Dimension  Note: to ease debugging, you may use three packages and test them one by one, instead of doing everything in one package 11

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 12

Load DimBatch Data Flow

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Load DimBatch Data Flow

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

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The 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

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Load Fact Data Flow

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Derived Columns for the Fact table

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Expressions for the Derived Columns    AcceptedProducts ◦ [NumberProduced] – [NumberRejected] ElapsedTimeForManufacture ◦ DATEDIFF(“mi”, [TimeStarted],[TimeStopped]) DateOfManufacture ◦ (DT_DBTIMESTAMP)SUBSTRING((DT_WSTR,25)[T imeStarted],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 Destination For loading the fact table

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Load Inventory Fact

   OLE DB Source ◦ OrderProcessingSystem.InventoryFact

OLE DB Destination ◦ MaxMinManufacturingDM-lastname.InventoryFact

No 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 21

Debugging Results

Loading DimBatch Loading ManufacturingFact 22

BUILDING AN OLAP CUBE

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Three Steps to Create a Cube from Data Sources     Defining data source Defining 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 table Customize the cube: ◦ Link two fact tables in a cube ◦ Define new primary key for Dim Time ◦ Define calculated measures ◦ Relate dimensions to measures 24

T-SQL Expressions for DS View Definition 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 MonthOfManufacture CONVERT(char(4), YEAR(DateOfManufacture)) + RIGHT('0'+CONVERT(varchar(2), MONTH(DateOfManufacture)),2) 25

T-SQL Expressions for DS View Definition 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 MonthOfInventory CONVERT(char(4), YEAR(DateOfInventory)) + RIGHT('0'+CONVERT(varchar(2), MONTH(DateOfInventory)),2) 26

Data Source View

New columns 27

Select Measures Page

Uncheck Manufacture

Fact Count

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Review New Dimensions Page

Rename Manufacturing Fact to Dim Time 29

New dimension Created from the Fact table

The finished cube

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Cube Structure

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MEASURES

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Facts

   Measurements 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 measures

  The 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. 34

Define Format String “#, #” for measures:

AcceptedProduct, RejectedProject 35

Defining a format string

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Define Calculated Measures

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DIMENSIONS

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Managing Dimensions

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Managing Dimensions

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Relating Dimensions to Measure Groups 43

Completed Dimension Definitions

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Types 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 45

CUBE DESIGN TABS

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Understanding the Cube Designer Tabs          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 47

Key Performance Indicators (KPIs)

  Digital dashboard Creating a KPI ISQS 6339, Data Mgmt & Business Intelligence 48

The MDX expression for KPI Status Expression (MaxMinManufacturingDM) Case When ROUND([Measures].[percent Rejected],4) < 0.0103

Then 1 When ROUND([Measures].[percent Rejected],4) >= 0.0103 ROUND([Measures].[percent Rejected],4) >= 0.0104 Then .5

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

End Else -1 AND AND ISQS 6339, Data Mgmt & Business Intelligence 49

Calculated measure 50

KPI definition and deployment 51

KPI Browser

Browser View

ISQS 6339, Data Mgmt & Business Intelligence 52

Actions

    Instructions stored inside the cube Allow 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.

ISQS 6339, Data Mgmt & Business Intelligence 53

Types of Actions

   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 ISQS 6339, Data Mgmt & Business Intelligence 54

Defining Actions

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Perspectives

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Translations

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Q & 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 Debugging     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? 59