Mastering Infocube Design

Mastering InfoCube Design

Luis Orama

Platinum Consultant

SAP

SAP AG 2002, Mastering InfoCube Design, Luis Orama, 2

Agenda

InfoCube Design: Key Concepts

InfoCube Design: Issues & Techniques

Tips and Tricks

9 Decision Points for Dimensional Data Warehouse Design

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Agenda



Tips and Tricks

9 Decision Points for Dimensional Data Warehouse Designè

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n The design of your data model is critical because it impacts

n Data load performancen Reporting performancen Hardware/database sizing

n The overall goals of the InfoCube design

n Offer information to end-users in a way that matches their normal understanding of the business

n Deliver structured information, enabling easy navigation/drill-downn Produce a model that can be easily implemented


InfoCube Design: Key Concepts (cont’d)

n BW data flow

n How data flows into BW & how users report on this data

n Multi-dimensional models & Star Schemas

n InfoCubes

n Fact, Dimension, and SID tables


Source systems BW

Reporting application

Key Concept #1 - BW Data Flow

BW Information Integration Architecture

SAPR/3APOCRMBBP

Legacy

ExternalProvider

Extraction

Master Data

ETL Tools

PSAPSA InfoCubesInfoCubes

Business Rules

Cleansing & Transformation

Business Rules

Granularity

Integration

BW• Business Explorer• Web• Graphical User Interf.

SAP Models• Advanced Planning• Enterprise Mgmt• CRM

Third-Party Tools(ODBC/ ODBO)

Metadata

BW Operational Data Store


1 - BW Data Flow (cont’d)

BW Information Access Architecture

Legacy

ExternalProvider

Master DataMaster Data

ETL Tools

PSA

BW Operational Data Store

InfoCubes

BW• Business Explorer• Web• Graphical User Interf.

SAP Models• Advanced Planning• Enterprise Mgmt• CRM

Third-Party Tools(ODBC/ ODBO)

SAPR/3APOCRMBBP

Source systems BW

Reporting application

Metadata


Key Concept #2 - MDMs and Star Schemas

n Multi-dimensional Data Models (MDM)

n Are used by most data warehousesn Store data in dimensionsn Meet reporting needs with minimum storage

u Minimize report runtimes

n Allow users to drill-down and slice-and-dice

n MDMs are also known as Star Schemas


Text

SID Tables

Master

Hierarchies

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Text

SID Tables

Master

Hierarchies

Text

SID Tables

Master

Hierarchies

Text

SID Tables

Master

Hierarchies

DimensionTable

Text

SID Tables

Master

Hierarchies

DimensionTable

DimensionTable

DimensionTable

DimensionTable

Hierarchies

Master

SID Tables

Text

FACT

2 - MDMs and Star Schemas (cont’d)


2 - MDMs and Star Schemas (cont’d)

n The SAP BW Schema n Uses a sophisticated approach to guarantee data

consistencyn Offers schema-based functionality for end users’ analysis

needs

n BW’s MDM consists of two partsn InfoCubesn Shared Master Data Tables

u Valid for all InfoProviders

Key

Feature


2 - MDMs and Star Schemas: Tips

n Consider using Business Content whenever possiblen BC InfoSources are a good starting point for identifying entities,

attributes and facts (key figures)

n It’s easy to identify the Business Content for your area n BW offers InfoProviders by InfoArea and InfoSources by

application

Great

Feature


Key Concept #3 - InfoCubes

n Data is stored in “InfoCubes” n InfoCubes are essentially optimized, mini-databases

BW

R/3

Tables optimized for data processing

Cubes optimized for reporting

Extraction


Key Concept #4 - Fact, Dimension, & SID Tables

n InfoCubes are represented via Fact and Dimension tablesn The Fact table contains key figures

u e.g., Net Sales was 1.23 million USD

n The Dimension tables contain characteristicsu e.g., The net sales was for material “CAL22MAST15A”

n Dimension tables use SID tables to save storage spacen SID tables contain SID IDs that represent master data

u e.g., SID #000222 may represent material “CAL22MAST15A”

n Each SID table can be used by > 1 InfoCube


4 - Fact, Dimension, & SID Tables (cont’d)

Text

SID Tables

Master

Hierarchies

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

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Text

SID Tables

Master

Hierarchies

Text

SID Tables

Master

Hierarchies

Text

SID Tables

Master

Hierarchies

DimensionTable

Text

SID Tables

Master

Hierarchies

DimensionTable

DimensionTable

DimensionTable

DimensionTable

Hierarchies

Master

SID Tables

Text

FACT


4 - Fact, Dimension, & SID Tables: Limitations

n Fact tables allown 16 Dimensionsn 3 Dimensions are SAP’s: Time, Unit and Packet IDn Can have up to 233 key figures assigned to them

n Dimension tablesn Can have up to 248 characteristics assigned to them


Agenda



Tips and Tricks


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InfoCube Design: The “Big” Picture

n InfoCube design involves balancing

n Granularity / Analysis needsn Data volumen Performance

n Keys to success

n Know your needsn Store only the data you need n Optimize the storage

n We will cover techniques for each...

AnalysisNeeds

Volumeof DataPerformance


InfoCube Design: Common Issues

n Rows are added to the fact table defined by the key attributes of the dimension table

n Changes to dimension attributes (slowly changing dimensions)

n These can have an impact on reporting views

n With large number of rows, fact tables and/or large dimension tables, precalculated aggregates must be introduced for performance reasons

Issue


InfoCube Design: Common Issues (cont’d)

n N:M relationships within a dimensionn e.g. material and material colorn There is no easy way to handle this

n No leaf attribute valuesn e.g. Material and material group in same dimensionn Facts are delivered at the material group leveln This results in null/blank values for the material

n Performance - Degenerate Dimensionsn Dimension tables should be at least 10 – 20% of the fact

tablen Larger than that and you could start having a degenerate

dimension (dimension table as large as the fact table)


InfoCube Design Issues We’ll Cover

n 1 - Building Your Data Model

n 2 - Using Line Item Dimensions

n 3 - Partitioning Fact Tables

n 4 - Using InfoCube Aggregates

n 5 - Enabling Data Compression


Technique #1 - Building Your Data Model

n 3 Key Steps

n Research and understand the underlying business processes

n Create a valid schema n Implement the schema

Building Block


1 - Building Your Data Model (cont’d)

Sales Rep ID

LastNameSalesDep

Material ID

Material NameMaterial TypeMaterial Group

Customer ID

Customer NameCityRegionOffice Name

Time Code ID

YearFiscal YearQuaterMounthDay of the Week

Material IDSales Rep IDTime Code IDCustomer ID

Sales AmountQuantityUnit Price

Time DimensionCustomer Dimension

Sales Org DimensionMaterial Dimension

FACT

Material DIM ID OrgStr DIM IDTime Code ID ....Quantity.....

SalesRep ID

Last Name...

Material DIM ID

Material IDMatType

Material ID

Mat.descriptionMatType...

OrgStr. DIM ID

SalesRep SalesDep SalesDep ID

Address...

l Focus on analytical needs -Overcome model complexity

l Build the solution as apart of an integrateddata warehouse

MDM/ Star Schema

SAP BW

ERM

l Focus on the structure of information


Technique #2 - Using Line Item Dimensions

n You can use Line Item Dimensions to improve performancen BW doesn’t generate a dimension table for these dimensionsn Less data gets extracted and stored

n Use when you expect your dimension table to be as large as the fact tablen e.g., a document dimension n Only one characteristic can be assigned to a line item dimension

since the characteristic’s SID becomes the key to the fact table

Key

Feature


2 - Using Line Item Dimensions (cont’d)

n How to do itn Use transaction RSA1 (Administrative workbench)n Navigate to the InfoProvider tabn Right-click on the InfoCube -> Select changen Navigate to the Dimension Tabn Select Line Item check box

Checklist


2 - Using Line Item Dimensions (cont’d)


Technique #3 - Partitioning Fact Tables

n Each InfoCube actually has two fact tablesn F – Fact Table – optimized for data loadingn E – Fact Table – optimized for retrieving data

n BW allows you to partition either tablen Partitioning splits the table into several tablesn This can speed up query performance significantlyn The F-fact table is already partitioned by the request’s IDn The E-Fact table can be partitioned by

u Calendar year month (0CALMONTH) u Fiscal year period (0FISCPER)u The InfoCube needs to be compressed in order to take

advantage of the partitioning. (This can be scheduled)


3 - Partitioning Fact Tables (cont’d)

TcodeRSA1

Go to Extras->Partitioning


3 - Partitioning Fact Tables: Tips

n Partitioning is fully transparent

n Partitioning has to be defined prior to activating the InfoCube

n Other Considerationsn Need to compressn Gets rid of packet IDn Can no longer manage individual data packets

Tip


Technique #4 - Using InfoCube Aggregates

n Aggregates are designed to speed up query performancen Aggregates are smaller cubesn They have the fact table definition and you can define the

dimensionsn Aggregates are transparent to the end user

n Queries will automatically select aggregates that are relevantn No manual action is needed

Aggregates increase space and “roll-up” time. You’ll need to actively maintain your aggregatesHeads Up!


Technique #5 - Enabling Data Compression

n Compressing tables improves performancen Moves the select fact records from the F- to the E-Fact Tablen You can schedule compression from the InfoCube

maintenance tab

When you do this, the request ID of each fact record is set to zero. You’ll lose capability to manage individual packet IDs

Warning


Technique #5 - Enabling Data Compression (cont’d)


Agenda


InfoCube Design: Issue & Techniques

Tips and Tricks


èèè


InfoCube Design: Tips and Tricks

n 1 - Consider the Level of Granularity

n 2 - Dimension Table Design Best Practices

n 3 - Strategically Locate Dependent Attributes

n 4 - Monitor Your InfoCubes


Tip #1: Consider the Level of Granularity

n Granularity greatly influencesn Reporting Capabilitiesn Performancen Space neededn Load time

n Key Questions to askn Does the data need to be stored in the cube?

u Storing data in an ODS offers a lower level of granularity

n Does the data need to be stored in the warehouse at all?u Can you meet users drill-down requirements by linking directly

to R/3?u This would avoid having to load and store the data in BW


Tip #2: Dimension Table Design Best Practices

n Put characteristics that have a parent/child relationship into a dimensionn Good rule of thumb

n Avoid N:M relationships in a dimensionn i.e., Customer and material

n Use Navigational Attributes vs. storing the characteristic in the dimensionn Depends on the reporting view neededn Frequency of reporting


2 - Dimension Table Design Best Practices (cont’d)

n When defining the dimension of an InfoCube, you select the InfoObjects of type char and assign them to a dimension

n BW does not force you to put only related characteristics in a dimension

n Once the InfoCube is activated, the dimension table(s) are generated

n The dimension table consists of the key Dim ID and the SIDs of the characteristic(s) assigned to the dimension

C u s tom e r D i m e n s i o n T a b le

D IM-ID

11223344556677

C u s t-S ID

1711171227113711471157116711

C o u n try-S ID

11112233445566



Fact Table

Mat-GR-SID Mat-SID Mat-DIM-ID

910 001 111

910 002 222

920 002 666

920 003 333

920 004 444

920 005 555

Mat-GR-SID Mat-SID Mat-DIM-ID

910 001 111

910 002 222

920 002 666

920 003 333

920 004 444

920 005 555920 005 555

Mat Mat-SID

AAA 001

BBB 002

CCC 003

DDD 004

EEE 005

Material Grp SID

Mat-GR Mat-GR-SID

X 910

Y 920

Material SID

Mat-DIM-ID Time-DIM-ID Revenue

111 09/1998 100

222 09/1998 100

333 09/1998 100

444 09/1998 100

111 10/1998 100

222 10/1998 100

333 10/1998 100

444 10/1998 100

555 10/1998 100

Mat-DIM-ID Time-DIM-ID Revenue

111 09/1998 100

222 09/1998 100

333 09/1998 100

444 09/1998 100

111 10/1998 100

222 10/1998 100

333 10/1998 100

444 10/1998 100

555 555 10/10/19981998 100100

Material Dimension Table

Fact Table

EEE Y 10/1998 100Transaction record

Add new record to dim table

Add new recordto fact table



112233

111111222222333333

Dim ID SID Material

Fact tableDimension table

Material not time dependentAttributes SID table(Name: /BIC/XMATERIAL)

Material not time dependentAttributes SID table(Name: /BIC/XMATERIAL)

Material MatGroup

AAACCCDDD

X Y Y

Material Master table(Name: /BIC/PMATERIAL)

Material Master table(Name: /BIC/PMATERIAL)

112233

10.00012.00025.000

Dim ID Sales

SID Material Material SID MatGroup

AAA CCC DDD

111111222222333333

345345678678678678

MatGroup SID MatGroup

X Y Z

345 678 999 MatGroup SID table

(Name: /BIC/SMATGROUP)

MatGroup SID table(Name: /BIC/SMATGROUP)Not used for Infocube access !

Example: Show me the sales values for material group X and Y

Not used in this Example :•Traditional Material SID Table: /BIC/SMATERIAL•Time dependent Material Master Table: /BIC/QMATERIAL•Material Time dependent Attributes SID Table: /BIC/YMATERIAL

Not used in this Example :•Traditional Material SID Table: /BIC/SMATERIAL•Time dependent Material Master Table: /BIC/QMATERIAL•Material Time dependent Attributes SID Table: /BIC/YMATERIAL

SID Tables for Infocube Access


Tip #3: Strategically Locate Dependent Attributes

n Where to model the dependant attributes: i.e., material group from materialn In the dimension as dimensional characteristicsn As navigational attributes from the materialn As a hierarchy from the material

Each of these scenarios can offer very different reporting views

Note


Tip #4: Monitor Your InfoCubes

n You can analyze your InfoCubes in BW to improve performancen load timen Roll-up time (aggregate build)n compression timen query timen Dim and fact table sizing, etc.

It’s very important to periodically analyze your queries and cubes to maintain optimal performanceNote


Tip #4: Monitor Your InfoCubes (cont’d)

n How to do itn Use transaction RSRV: Analysis on any object in BWn Use transaction RSRT: Query analysis tooln BW Statistics InfoCube


Agenda


InfoCube Design: Issues Techniques

Tips and Tricks


èèè


9 Decision Points for Dimensional Data Warehouse Design

n The processes, and hence the identity, of the Fact Tables u one Fact Table - one InfoCube... -> intersection entities

n The dimensions of each Fact Table u strong entities

n The dimension attributes with complete descriptions and proper terminologyu attributes and entities

n The grain of each Fact Table

Source: Ralph Kimball “The Data Warehouse Toolkit”


Conclusion (cont’d)

n The nine design decision points for a dimensional DW (cont’d)

n The facts, including precalculated facts n How to track slowly changing dimensions n The aggregations, heterogeneous dimensions, mini-dimensions,

query modes and other physical storage decisionsn The historical duration of the database

u archiving aspects

n The urgency with which the data is extracted and loaded into thedata warehouse u time frame for loading

n Source: Ralph Kimball “The Data Warehouse Toolkit”


Your Turn!

Questions?


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