InfoCubes and Aggregates

UNWBW1 – Business Information Warehouse

NetWeaver Support Consultant Training

InfoCubesand Aggregates

SAP AG 2004, Business Information Warehouse / 2

Content

Introduction

Reporting

Business content

Data loading

InfoCube Design

Aggregates

BW-BPS Business Planning & Simulation

Monitoring & Technical Risks


Star Schema

Star Schema(Logical)

T

ime

Customer Dimension

Pro

du

ct

Dim

en

sio

n

Product Dimension

Quantities Revenues

CostsRev./Group

Customer Dimension

Sales Dimension

Competition Dimension

Time Dimension


Time dimensionProduct dimension

Customer dimension

P Product # Product group …

2101004 Displays ...

C Customer # Region …

13970522 West ...

T Period Fiscal year …

10 1999 ...

Dimensions

Dimension tables are groupings of related characteristics.

A dimension table contains a generated primary key and characteristics.

The keys of the dimension tables are foreign keys in the fact table.


CustomerCustomer number

Customer name

Cust Category

Cust Subcategory

Division

Industry

Revenue Class

Transportation zone

Currency

VAT #

Legal Status

Regional market

Cust Statistics group

Incoterms

Billing schedule

Price group

Delivering plan

ABC Classification

Account assignment group

Address

State

Country

Region

ProductMaterial number

Material text

Material type

Category

Subcategory

Market key

MRP Type

Material group 1

Planner

Forecast model

Valuation class

Standard cost

Weight Volume

Storage conditions

Creation Date

SalesSalesperson

Rep group

Sales territory

Sales region

Sales district

Sales planning group

Distribution key

CompetitionNielsen indicator

SEC Code

Primary competitor

Secondary Competitor

Time

Date

Week

Month

Fiscal Year

Example: Sales Infocube Dimensions


P C T Quantity Revenue Discount Sales overhead

250 500,000 $ 50,000 $ 280,000 $

50 100,000 $ 7,500 $ 60,000 $

… … … ...

Fact table

Fact Table

A record of the fact table is uniquely defined by the keys of the dimension tables

A relatively small number of columns (key figures) and a large number of rows is typical for fact tables

A fact table is maintained during transaction data load


Facts - Sales

Quantity soldList priceDiscountsInvoice priceFixed mfg. costVariable costMoving average priceStandard costContribution marginExpected ship dateActual ship date

Example: Sales Facts


Facts

Qty soldList priceDiscountsInvoice priceFixed mfg costVariable costMoving average priceStandard costContribution marginExpected ship dateActual ship date

CustomerMaterialCompetitionSalesTime

Competition

Nielsen indicator

SEC Code

Primary competitor

Secondary Competitor

Sales

Salesperson

Rep group

Sales territory

Sales region

Sales district

Sales planning group

Distribution key

Time

Date

Week

Month

Fiscal Year

Customer

Customer number

Customer name

Cust. Category

Cust. Subcategory

Division

Industry

Revenue Class

Transportation zone

Currency

VAT #

Legal Status

Regional market

Cust. Statistics group

IncoTerms

Billing schedule

Price group

Delivering plan

ABC Classification

Account assignment group

Address

State

Country

Region

Material

Material number

Material text

Material type

Category

Subcategory

Market key

MRP Type

Material group 1

Planner

Forecast model

Valuation class

Standard cost

Weight Volume

Storage conditions

Creation Date

Sales

Example: Sales Star Schema


Only characteristics of the dimension tables can be used to access facts.

No structured drill downs can be created.

Support for many languages is difficult.

In a basic Star Schema we are limited:

Master data tables and their associated fields (attributes).

Text tables with extensive multilingual descriptions.

External hierarchy tables for structured access to the data.

In BW, the Extended Star Schema adds access to:

Extending the Star Schema


SAP BW: Extended Star Schema

DIM_ID_PACKAGEDIM_ID_TIMEDIM_ID_UNITDIM_ID_MATERIALDIM_ID_CUSTOMER

AmountSales

Fact Table

DIM_ID_PACKAGE

SID_REQUEST

Datapackage Dimension Table

REQUEST_ID

SID_REQUEST

RequestSID-Table

DIM_ID_TIME

SID_MONTHSID_YEAR

Time Dimension Table

MONTH_ID

SID_MONTH

Calendar MonthSID-Table

YEAR_ID

SID_YEAR

Calendar YearSID-Table

DIM_ID_UNIT

SID_AMOUNTSID_CURRENCY

UnitDimension Table

CURRENCY_ID

SID_CURRENCY

CurrencySID-Table

AMOUNT_ID

SID_AMOUNT

AmountSID-Table

DIM_ID_MATERIAL

SID_MATERIAL

MaterialDimension Table

MATERIAL_ID

SID_MATERIAL

MaterialSID-Table

MATERIAL_ID

Material Group

Material Attributes Table

MATERIAL_ID

Material Name

Material Text Table

external Material HierarchyDIM_ID_CUSTOMER

SID_CUSTOMER

CustomerDimension Table

CUSTOMER_ID

CityRegion

Customer Attributes Table

CUSTOMER_ID

Customer Name

Customer Text Table

CUSTOMER_ID

SID_CUSTOMER

Customer SID-Table InfoCubeInfoCube


Dimensions

up to 16 dimensions

3 dimensions exist with each InfoCube (whether they are used and thus visible or not)

Time dimension Unit dimension Packet dimension

The remaining 13 dimensions are for individual schema design

Each dimension table may be up to 248 characteristics.

Gebiet 1Gebiet 2Gebiet 3

Bezirk 1

Gebiet 3a

Bezirk 2

Region 1

Gebiet 4Gebiet 5

Bezirk 3

Region 2

Gebiet 6

Bezirk 4

Gebiet 7Gebiet 8

Bezirk 5

Region 3

Vertriebsorganisation

Material Group

Material Hierarchy Table

Material NumberLanguage Code

Material NumberLanguage Code

Material Name

Material Text TableMaterial_Dimension_ID

Material Number

Material Dimension Table

Material Attribute Table

Material NumberMaterial Number

Material Type

MaterialMaterial Dimension Dimension


Summary

The center of a multidimensional schema in BW are the fact tables.

The fact tables are surrounded by dimensions.

Dimension Table In BW the attributes of the dimension tables are called characteristics

(e.g. material). Master Data Tables:

Attribute TablesDependent attributes of a characteristic can be stored in an Attribute Table for the characteristic.

Text TablesTextual descriptions of a characteristic are stored in a separate text table.

External Hierarchy TablesHierarchies of characteristics or attributes may be stored in separate hierarchy tables.


Compressing the InfoCube

Records added to InfoCube fact tables have several “keys” which uniquely identify the record.

Request ID is just one of several fields in a record that helps identify the data.

But, Request ID can be removed, and each record can still be uniquely identified.

Compression finds records which are identical except for Request ID, then aggregates these to one single record.

If a compression is not performed, the “Group by” condition of any query’s SQL statement will remove duplicates. This results in decreased query performance.


Compressing the InfoCube

Request IDs Lost !!!

Request Date Record Cost

1 01.01.2002 1 100

1 02.06.2002 2 200


2 01.01.2002 1 200

2 04.10.2002 2 300


0 01.01.2002 1 300

0 02.06.2002 2 200

0 04.10.2002 2 300

COMPRESSION

E-Fact table

F-Fact table


Content

Introduction

Reporting

Business content

Data loading

InfoCube Design

Aggregates

BW-BPS Business Planning & Simulation

Monitoring & Technical Risks


Aggregates ...

... are like InfoCubes,

... are always based on InfoCubes

... summarize ("aggregate") data of the originating InfoCube,

... contain redundant information, but

... accelerate the access to that information,

... are performance-enhancing features.


Aggregates - Example

Country Customer Sales

USAGermanyUSAAustriaAustriaGermanyUSA

Buggy Soft Inc.Ocean NetworksFunny Duds Inc.Ocean NetworksThor IndustriesFunny Duds Inc.Buggy Soft Inc.

1015510102025

Fact Table: Sales Data Aggregate Tables: Sales Data

Country *

Country Sales

403520

USAGermanyAustria

Data for queries like ‘sales for all countries’, ‘sales in Germany’, or ‘overall sales’ can be read out of the aggregate (country *).


Aggregates - Example using filters




1015510102025


Country GermanyCustomer *

Country Sales

1520

GermanyGermany

Customer

Ocean NetworksFunny Duds Inc.

Data for queries like ‘sales for all customers in Germany'can be read out of the aggregate (country =Germany; customer=*)


Aggregates - Example using master data




1015510102025


Industry *

Industry Sales

602510

TechnologyConsumer ProductsChemical

TechnologyConsumer ProductsTechnologyChemical

IndustryCustomer

Buggy Soft Inc.Funny Duds Inc.Ocean NetworksThor Industries

Attribute Table: Customer


Aggregates - Example using hierarchies




1015510102025


Country Hierarchy, Level 2

Country Sales

4055

AmericaEurope

All

Europe America

Germany Austria USA

Hierarchy for Country


Aggregates - Maintenance

Switch on/off

Show aggregate hierarchy

BDS

Transport

Activate & Fill

unsaved changes


Aggregate Maintenance

After new data is loaded existing aggregates have to be adjusted in order to make the new data available for reporting:

Aggregate Rollup:

The newly uploaded transactional data is added to the aggregates

Changerun (Master Data Activation):

The newly uploaded master data is applied to the aggregates and activated. During the change run, all aggregates containing navigational

attributes and/or hierarchies are realigned

Documents

InfoCubes and Aggregates