Types Of DSO'S

8/18/2019 Types Of DSO'S

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'his graphic sho%s ho% the various tables of the DataStore object %ork together during the dataload

Data can be loaded from several source systems at the same time because a 3ueuing mechanism

enables a parallel I!S4-' 'he key allo%s records to be labeled consistently in the activation

3ueue

'he data arrives in the change log from the activation 3ueue and is %ritten to the table for active

data upon activation During activation, the re3uests are sorted according to their logical keys

'his ensures that the data is updated to the table of active data in the correct re3uest se3uence

DataStore Data and !"terna Appications

'he BAPI, BAPI7.DS.7-4AD7DA'A705, for reading data, enables you to make DataStore

data available to e*ternal systems

Write-Optimized DataStore Objects

De#inition

A DataStore object that consists of just one table of active data Data is loaded using the data

transfer process

Use


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Data that is loaded into %rite(optimi2ed DataStore objects is available immediately for further

processing

'hey can be used in the follo%ing scenarios&

● 8ou use a %rite(optimi2ed DataStore object as a temporary storage area for large sets of dataif you are e*ecuting comple* transformations for this data before it is %ritten to the DataStoreobject 'he data can then be updated to further smaller/ InfoProviders 8ou only have to create

the comple* transformations once for all data

● 8ou use %rite(optimi2ed DataStore objects as the 4DW layer for saving data Business rulesare only applied %hen the data is updated to additional InfoProviders

'he system does not generate SIDs for %rite(optimi2ed DataStore objects and you do not need to

activate them 'his means that you can save and further process data 3uickly -eporting is possible on the basis of these DataStore objects 9o%ever, %e recommend that you use them as a

consolidation layer, and update the data to additional InfoProviders, standard DataStore objects,or Info5ubes

Structure

Since the %rite(optimi2ed DataStore object only consists of the table of active data, you do not

have to activate the data, as is necessary %ith the standard DataStore object 'his means that youcan process data more 3uickly

'he loaded data is not aggregated6 the history of the data is retained If t%o data records %ith the

same logical key are e*tracted from the source, both records are saved in the DataStore object

'he record mode responsible for aggregation remains, ho%ever, so that the aggregation of datacan take place later in standard DataStore objects

'he system generates a uni3ue technical key for the %rite(optimi2ed DataStore object 'he

standard key fields are not necessary %ith this type of DataStore object If there are standard keyfields any%ay, they are called semantic keys so that they can be distinguished from the technical

keys 'he technical key consists of the -e3uest :0ID field #-4;04S'/, the Data Package

field #DA'APA


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Use in $!" %ueries

>or performance reasons, SID values are not created for the characteristics that are loaded 'he

data is still available for B4* 3ueries 9o%ever, in comparison to standard DataStore objects,you can e*pect slightly %orse performance because the SID values have to be created during

reporting

If you %ant to use %rite(optimi2ed DataStore objects in B4* 3ueries, %e recommend that they

have a semantic key and that you run a check to ensure that the data is uni3ue In this case, the%rite(optimi2ed DataStore object behaves like a standard DataStore object If the DataStore

object does not have these properties, you may e*perience une*pected results %hen the data is

aggregated in the 3uery

DataStore Data and !"terna Appications

'he BAPI, BAPI7.DS.7-4AD7DA'A705, for reading data, enables you to make DataStoredata available to e*ternal systems

Since write-optimized Data Store objects do not have a change log, the system does not create d and an after

image). When you update data into the connected nfo !roviders, the system only been posted

t was mainly to improve the "oading !erformance. #ost of the clients they will have $bstract "a copy of the Datain Source System or Dump and %eload for "oo&ups, in these cases Write 'ptima performance and no need ofDS' $ctivation.

Delta $dministration(

Data that is loaded into Write-'ptimized Data Store objects is available immediately for further p has been

necessary up to now is no longer reuired. *ote here that the loaded data is not agree same logical &ey aree+tracted from the source, both records are saved in the Data records not uniue. he record mode%/0'%D#'D/) responsible for aggregation remains, ta&e place at a later time in standard Data Store objects.

Write-'ptimized DataStore supports reuest level delta, and you will get brand new delta reuest for each dataload.

Since write-optimized DataStore objects do not have a change log, the system does not create dand an after image). When you update data into the connected nfo !roviders, the system only been posted.

Write-'ptimized Data Store supports reuest level delta. n order to capture before and after image latest reuestinto further targets li&e Standard DataStore or nfo cubes.

Write-'ptimized DS' has been primarily designed to be the initial staging of the source system transferred to

the Standard DS' or the nfo 0ube. he data is saved in the write-optimized Data Store object uic&ly. Data is

stored in at most gran items are e+tracted using a Data Source and stored in the DataStore. he data is then

immediately written to the further data targets in the architected data mart layer analysis. he &ey benefit of using

write-optimized DataStore object is that the data is immediately available version. 1'2 S$3/ $03$'* #/

across the landscape.


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Delta in write optimized DS'

Since write-optimized DataStore objects do not have a change log, the system does not create d and an after

image). When you update data into the connected nfo !roviders, the system only u been posted.

I %ould like to focus on the features, usage and the advantages of Write-Optimzied DataStore.

Write(.ptimi2ed DS. has been primarily designed to be the initial staging of the source systemdata from %here the data could be transferred to the Standard DS. or the Info5ube

o 'he data is saved in the %rite(optimi2ed Data Store object 3uickly Data is stored in at

most granular form Document headers and items are e*tracted using a DataSource and stored in

the DataStore

o 'he data is then immediately %ritten to the further data targets in the architected data

mart layer for optimi2ed multidimensional analysis

'he key benefit of using %rite(optimi2ed DataStore object is that the data is immediately

available for further processing in active version 8.0 SA?4 A5'I?A'I.! 'I@4 across thelandscape 'he system does not generate SIDs for %rite(optimi2ed DataStore objects to achive

faster upload -eporting is also possible on the basis of these DataStore objects 9o%ever, SAP

recommends to use Write(.ptimi2ed DataStore as a 4DW inbound layer, and update the data

into further targets such as standard DataStore objects or Info5ubes

&ast !DW inbound a'er - An (ntroduction

Data %arehousing has been developed into an advanced and comple* technology >or some time

it %as assumed that it is sufficient to store data in a star schema optimi2ed for reporting

9o%ever, this does not ade3uately meet the needs of consistency and fle*ibility in the long run'herefore data %arehouses are structured using layer architecture like 4nterprise data %arehouse

layer and Architectured data mart layer 'hese different layers contain data at different levels of

granularity as sho%n in &igure )


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&igure ) 4nterprise Data Warehouse ayer is a corporate information repository

'he benefit of 4nterprise Data %arehouse ayer includes the follo%ing&

-eliability, 'race back ( Prevent Silos

o Single point of truth

o All data have to pass this layer on its path from the source to the summari2ed 4DW

managed data marts5ontrolled 4*traction and Data staging transformations, cleansing/

o Data are e*tracted only once and deployed many

o @erging data that are commonly used together

>le*ibility, -eusability and 5ompleteness

o 'he data is not manipulated to please specific project scopes unflavored/

o 'he coverage of une*pected adhoc re3uirements

o 'he data is not aggregated


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o !ormally not used for reporting, used for staging, cleansing and transformation one time

o .ld versions like document status are not over%ritten or changed but useful information

may be added

o 9istorical completeness ( different levels of completeness are possible from availabilityof latest version %ith change date to change history of all versions including e*traction history

o @odeled using Write(.ptimi2ed DataStore or standard DataStore

Integration

o Data is integrated

o -eali2ation of the corporate data integration strategy

Architectured data marts are used for analysis reporting layer, aggregated data, data manipulation%ith business logic, and can be modeled using Info5ubes or @ulti 5ubes

When is it recommended to use Write-Optimized DataStore

9ere are the Scenarios for Write(.ptimi2ed DataStore As sho%n in &igure */

o >ast 4DW inbound layer

o SAP recommends Write(.ptimi2ed DS. to be used as the first layer It is called4nterprise Data Warehouse layer As not all business content come %ith this DS. layer, you

may need to build your o%n 8ou may check in table -SD.DS. for version D and type =Write(.ptimi2ed=

o 'here is al%ays the need for faster data load DS.s can be configured to be Writeoptimi2ed 'hus, the data load happens faster and the load %indo% is shorter

o 0sed %here fast loads are essential 4*ample& multiple loads per day or/ short source

system access times %orld %ide system landscapes/

o If the DataSource is not delta enabled In this case, you %ould %ant to have a Write(

.ptimi2ed DataStore to be the first stage in BI and then pull the Delta re3uest to a cube

o Write(optimi2ed DataStore object is used as a temporary storage area for large sets of

data %hen e*ecuting comple* transformations for this data before it is %ritten to the DataStore

object Subse3uently, the data can be updated to further InfoProviders 8ou only have to create

the comple* transformations once for all incoming data

o Write(optimi2ed DataStore objects can be the staging layer for saving data Business

rules are only applied %hen the data is updated to additional InfoProviders


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o If you %ant to retain history at re3uest level In this case you may not need to have PSA

archive6 instead you can use Write(.ptimi2ed DataStore

o If a multi dimensional analysis is not re3uired and you %ant to have operational reports,you might %ant to use Write .ptimi2ed DataStore first, and then feed data into Standard

Datastore

o Probably you can use it for preliminary landing space for your incoming data from

diffrent sources

o If you %ant to report daily refresh data %ith out activationIn this case it can be used inreporting layer %ith InfoSet or/ @ultiProvider

I have discussed possible scenarios but re3uest you to decide %here this data store can fit in your

data flo%

+'pica Data &o, using Write-Optimized DataStore

&igure * 'ypical Data flo% using %rite(optimi2ed DataStore

&unctionait' o# Write-Optimized DataStore As sho%n in &igure /


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.nly active data table DS. key& re3uest ID, Packet !o, and -ecord !o/&

o !o change log table and no activation 3ueue

o Si2e of the DataStore is maintainable

o 'echnical key is uni3ue

o 4very record has a ne% technical key, only inserts

o Data is stored at re3uest level like PSA table

!o SID generation&

o -eporting is possiblebut you need make sure performance is optimi2ed /

o B4* -eporting is s%itched off

o 5an be included in InfoSet or @ultiprovider

o Performence improvement during dataload

>ully integrated in data flo%&

o 0sed as data source and data target

o 4*port into info providers via re3uest delta

0ni3ueness of Data&

o 5heckbo* CDo not check 0ni3ueness of data

o If this indicator is set, the active table of the DataStore object could contain several

records %ith the same key

Allo%s parallel load

5an be included in Process chain %ith out activation step

Support Archive

8ou cannot use reclustering for %rite(optimi2ed DataStore objects since this DataStore data is

not meant for 3uerying 8ou can only use reclustering for standard DataStore objects and the

DataStore objects for direct update


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PSA and Write optimi2ed DS. are the t%o different entities in the data flo% as each one has its

o%n features and usage Write optimi2ed DS. %ill not replace the PSA in a data flo% but it

allo%s to stage or/ store the data %ithout activation and to apply business rules

Write(optimi2ed DataStore .bject is automatically partitioned @anual Partitioning can be done

according to SAP !otes EFE)"EG)$""$1 .ptimi2ed Write performance has been achieved byre3uest level insertions, similarly like > table in Info5ube As %e are a%are that > fact table is

%rite(optimi2ed %hile the 4 fact table is read optimi2ed

&igure .vervie% of various DataStore objects types in BI )#

'o define Write(.ptimi2ed DataStore, just change C'ype of DataStore .bject to CWrite(

.ptimi2ed as sho%n in &igure .


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&igure 'echnical settings for Write(.ptimi2ed DataStore

Understanding Write-Optimized DataStore /e's:

Since data is %ritten into Write(optimi2ed DataStore active(table directly, you may not need to

activate the re3uest as is necessary %ith the standard DataStore object 'he loaded data is notaggregated6 the history of the data is retained at re3uest level If t%o data records %ith the same

logical key are e*tracted from the source, both records are saved in the DataStore object 'herecord mode responsible for aggregation remains, ho%ever, the aggregation of data can take place later in standard DataStore objects

'he system generates a uni3ue technical key for the %rite(optimi2ed DataStore object 'he

technical key consists of the -e3uest :0ID field #-4;04S'/, the Data Package field

#DA'APA


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ma*imum of +F key fields and )$H data fields are permitted Semantic or e*ample, if you define =order number= and Citem as the key, if you have one erroneous record %ith an order

number +"1$EF item ), then any other records received in that same re3uest or subse3uent

re3uests %ith order number +"1$EF item ) %ill also be detained 'his is applicable for duplicaterecords as %ell

&igure 0 Semantic group in data transfer process

Semantic key definition integrates the %rite(optimi2ed DataStore and the error stack through the

semantic group in D'P as sho%n in &igure 0 With SAP !etWeaver "##$s BI SPS+#, the %rite(

optimi2ed DataStore object is fully connected to the D'P error stack functionIf you %ant to use %rite(optimi2ed DataStore object in B4* 3ueries, it is recommend that you

define semantic key and that you run a check to ensure that the data is uni3ue In this case, the%rite(optimi2ed DataStore object behaves like a standard DataStore object If the DataStoreobject does not have these properties, une*pected results may be produced %hen the data is

aggregated in the 3uery

Deta Administration:


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Data that is loaded into Write(.ptimi2ed Data Store objects is available immediately for further

processing 'he activation step that has been necessary up to no% is no longer re3uired !ote

here that the loaded data is not aggregated If t%o data records %ith the same logical key aree*tracted from the source, both records are saved in the Data Store object, since the technical key

for the both records not uni3ue 'he record mode Info.bject #[email protected]

space,J,A,D,-// responsible for aggregation remains, ho%ever, the aggregation of data can take place at a later time in standard Data Store objects or/ Info5ube Write(.ptimi2ed DataStore

does not support the image based [email protected]/, it supports re3uest level delta, and

you %ill get brand ne% delta re3uest for each data load When you load a DataStore object that isoptimi2ed for %riting, the delta administration is supplied %ith the change log re3uest and not the

load re3uest.

Since %rite(optimi2ed DataStore objects do not have a change log, the system does not create

delta in the sense of a before image and an after image/ When you update data into theconnected InfoProviders, the system only updates the re3uests that have not yet been posted

Write(.ptimi2ed Data Store supports re3uest level delta In order to capture before and afterimage delta, you must have to post latest re3uest into further targets like Standard DataStore or

Infocubes

!"traction method - +rans#ormations thru D+1 2or3 Update 4ues thru (n#oSource

Prior to using D'P, you must have to migrate 1* DataSource into BI )# DataSource by using

transaction code C-SDS as sho%n in &igure 5

&igure 5 @igration of 1* Data Source (K Data Source using 'code C-SDS, and then replicate

the data source into BI )#

After data source replication into BI )#, you may have to create data transfer process D'P/ to

load data into Write(.ptimi2ed DataStore Write(optimi2ed DataStore objects can force a check


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of the semantic key for uni3ueness %hen data is stored If this option is active and if duplicate

records are loaded %ith regard to semantic key, these are logged in the error stack of the Data

'ransfer Protocol D'P/ for further evaluation

In BI) you are having the option to create error D'P If any error occurs in data, the error data

%ill be stored in 4rror stack So, you can correct the errors in stack, and if you schedule the error D'P, the error data %ill be stored to target .ther%ise, you have to delete the error re3uest from

target and you need to reschedule the D'P In order to integrate Write(.ptimi2ed DataStore into4rror stack, you must have to define semantic keys in DataStore definition and create semantic

group in D'P as sho%n in &igure 0

Semantic group definition is necessary to do parallel loads to Write(.ptimi2ed DataStore 8oucan update %rite(optimi2ed DataStore objects in parallel after you have implemented .SS

)667758 note When you include a D'P in process chain for %rite(optimi2ed DataStore .bject,

you %ill need to make sure that there is no subse3uent activation step for this DataStore

.n the other hand you can just link this DS. thru the Infosource %ith update rules as %ell byusing 1* functionality

4eporting Write-Optimized DataStore Data:

>or performance reasons, SID values are not created for the characteristics that are loaded 'he

data is still available for B4* 3ueries 9o%ever, in comparison to standard DataStore objects,

you can e*pect slightly %orse performance because the SID values have to be created duringreporting 9o%ever, it is recommended that you use them as a staging layer, and update the data

to standard DataStore objects or Info5ubes

.AP B4* 3uery perspective, there is no big difference bet%een Write(.ptimi2ed DataStore andStandard DataStore, the technical key is not visible for reporting, so the look and feel is just likeregular DataStore If you %ant to use %rite(optimi2ed DataStore object in B4* 3ueries, it is

recommended that they have a semantic key and that you run a check to ensure that the data is

uni3ue In this case, the %rite(optimi2ed DataStore object behaves like a standard DataStoreobject If the DataStore object does not have these properties, une*pected results may be

produced %hen the data is aggregated in the 3uery

In a nut shell, Write .ptimi2ed DS. is not for reporting purpose unless other%ise re3uired to do

so, its a staging DataStore used for faster upload 'he direct reporting on this object is also possible %ithout activation but keeping in mind the performance, you can use an infoset or multi(

provider

Concusion:

0sing Write(.ptimi2ed DataStore, you %ill have snapshot for each e*traction 'his data can beused for trending old


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@oreover you need not %orry about the status of e*tracted documents into BI since data is stored

as of e*tracted dateGtime >or e*ample .rder(to(5ashGSpend analysisetc life cycle can be

monitored in detail to identify the bottlenecks in the process

Although there is help documentation available from SAP on Write(.ptim2ied DataStore, I

thought it %ould be useful to %rite this blog that gives a clear vie% on Write(.ptimi2ed

DataStore concept, the typical scenarios of %here, %hen and ho% to use6 you can customi2e thedata flo%G data model as per reportingor/do%nstream re3uirement A more detailed step(by(step

technical document %ill be released soon

Use#u OSS notes:

Please check the latest .SS notes G support packages from SAP to overcome any technical

difficulties occurred and make sure to implement them

.SS +#))1#L& In a %rite(optimi2ed DataStore object, #>IS5?A-!' is treated as a key, even

though it is only a semantic key

DataStore Objects for Direct Update

Denition

The DataStore object for direct update diers from the standard DataStore object in

terms of how the data is processed. In a standard DataStore object, data is stored in

dierent versions active, de!ta, modied", whereas a DataStore object for directupdate contains data in a sin#!e version. Therefore, data is stored in precise!$ the

same form in which it was written to the DataStore object for direct update b$ the

app!ication. In the %I s$stem, $ou can use a DataStore object for direct update as a

data tar#et for an ana!$sis process. &ore information' (na!$sis )rocess Desi#ner.

The DataStore object for direct update is a!so re*uired b$ diverse app!ications, such

as S() Strate#ic +nterprise &ana#ement S+&" for eamp!e, as we!! as other

eterna! app!ications.

Structure

The DataStore object for direct update consists of a tab!e for active data on!$. It

retrieves its data from eterna! s$stems via !! or de!ete ()Is.

The fo!!owin# ()Is eist'

● -SD-I7.DS.7I!S4-'& Inserts ne% data %ith keys not yet in the system/

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● -SD-I7.DS.7I!S4-'7->5& see above, can be called up remotely

● [email protected]>8& inserts data having ne% keys6 for data %ith keys already in the

system, the data is changed

● [email protected]>87->5& see above, can be called up remotely

● -SD-I7.DS.70PDA'4& changes data %ith keys in the system

● -SD-I7.DS.70PDA'47->5& see above, can be called up remotely

● -SD-I7.DS.7D44'47->5& deletes data

The !oadin# process is not supported b$ the %I s$stem. The advanta#e to the wa$ it

is structured is that it is eas$ to access data. Data is made avai!ab!e for ana!$sis and

reportin# immediate!$ after it is !oaded.

-reatin# a DataStore Object for Direct Update

hen $ou create a DataStore object, $ou can chan#e the DataStore object t$pe

under Settings in the contet menu. The defau!t settin# is Standard. /ou can on!$

switch between DataStore object t$pes Standard and Direct Update if data does not

$et eist in the DataStore object.

Inte#ration

Since $ou cannot use the !oadin# process to !! DataStore objects for direct update

with %I data DataSources do not provide the data", DataStore objects are not

disp!a$ed in the administration or in the monitor. 0owever, $ou can update the data

in DataStore objects for direct update to additiona! Info)roviders.

If $ou switch a standard DataStore object that a!read$ has update ru!es to direct

update, the update ru!es are set to inactive and can no !on#er be processed.

Since a chan#e !o# is not #enerated, $ou cannot perform a de!ta update to the

Info)roviders at the end of this process.

The DataStore object for direct update is avai!ab!e as an Info)rovider in %+ 1uer$Desi#ner and can be used for ana!$sis purposes.

(na!$sis )rocess Desi#ner

Use


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The (na!$sis )rocess Desi#ner is a wor2bench with an intuitive #raphica! user

interface for creatin#, eecutin#, and monitorin# ana!$sis processes. The ana!$sis

process is primari!$ based on data that was conso!idated in the Data arehouse and

that eists in Info)roviders.

The data can be mer#ed from a number of sources, chan#ed step3b$3step usin#dierent transformation methods, and disp!a$ed in new views. The resu!ts of the

ana!$sis are then stored, for eamp!e in an Info)rovider or in a -4& s$stem. It is

then avai!ab!e for a!! decision and app!ication processes. The (na!$sis )rocess

Desi#ner is used especia!!$ in c!osed3!oop scenarios.

The transformation methods oer, for eamp!e, basic operations such as !ter, join

or formu!as, as we!! as advanced methods for data minin#. The data minin#

methods support $ou when $ou ep!ore and identif$ meanin#fu! re!ationships in

$our data.

&ore information' Data &inin#

+amp!es of ana!$sis processes inc!ude ca!cu!atin# (%- c!asses and determinin#

fre*uenc$ distribution or scorin# information.

/ou can use *ueries, database tab!es, and !es as we!! as Info)roviders as data

sources in the ana!$sis process to perform ad hoc ana!$ses. /ou can a!so store the

resu!ts of the ana!$sis direct!$ in a !e. 5ote the data *ua!it$ of the sources $ou use,

since the$ norma!!$ wi!! not have been inc!uded in the +T6 process of the Data

arehouse.

7eatures

(na!$sis processes can be created on a #raphica! user interface usin# dra# and

drop. Data from dierent data sources in the %I s$stem can be combined,

transformed, and prepared for ana!$sis in severa! individua! steps. This a!!ows it to

be resaved in data tar#ets in the %I s$stem DataStore objects for direct update or

InfoObjects with attributes" or in a -4& s$stem. 8arious

● Data sources

Data Sources for an (na!$sis )rocess

Denition

)rovides the input data for an ana!$sis process.

Use

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Dependin# on the dened prob!em or tas2 that $ou wish to so!ve with an ana!$sis

process, it is necessar$ to provide a!! re!evant data to the ana!$sis process from the

be#innin#. This means there is a su9cient comprehensive :raw data: basis upon

which the subse*uent steps can be based.

The fo!!owin# t$pes of data sources are avai!ab!e in the ana!$sis process desi#ner'

• Attribute of a 5haracteristic

• InfoProviders

• ;uery

• >ile

• Database 'able

● 'ransformations

Transformations for an (na!$sis )rocess

Use

Under Transformations, $ou wi!! nd functions for the preparation of data, as we!! asfunctions for transformation of data.

)reparation of data'

( comp!ete, error3free data basis is decisive for the #ood *ua!it$ of resu!ts of an

ana!$sis process. In order to be ab!e to #uarantee this, there are functions avai!ab!e

that $ou can use to prepare the data basis accordin#!$.

Transformation of the data'

Usin# ;rea!< transformations, it is then possib!e to uncover and map hiddeninformation.

7eatures

The fo!!owin# functions are avai!ab!e for the preparation of data in the ana!$sis

process desi#ner'

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● -estrict Amount of Data

● Aggregate Data

● Moin Data from @ultiple Sources

● 0nify Data from '%o Data Sources 0nion/

● 9ide 5olumns

● Sort Data

● >ormula

● 'ransform ist into Data -ecord

● 'ransform Data -ecord into ist

The fo!!owin# functions are avai!ab!e for the transformation of data in the ana!$sis

process desi#ner'

● ABAP -outine

● Data @ining @ethods&

•

= (%- c!assication• = ei#hted tab!e scorin#

• = 4e#ression ana!$sis

• = )rediction with decision tree

• = )rediction with c!uster mode!

• = )rediction with data3minin# mode! from third parties

● Data targets

Data Tar#ets for an (na!$sis )rocess

Use

The prepared or transformed data of an ana!$sis process is saved in a data tar#et.

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7eatures

The fo!!owin# t$pes of data tar#et are avai!ab!e in (na!$sis )rocess Desi#ner'

● Attributes of a characteristic

● DataStore objects

● >ile

● 5-@ attributes

● 'arget groups for SAP 5-@& 'his data target is only available in the Survey application

● Data mining models&

• = Trainin# the decision tree

• = Trainin# the c!usterin# mode!

• = Trainin# the scorin# mode! re#ression"

• = Trainin# data minin# mode!s from third parties

• = -reatin# association ana!$sis mode!s

are avai!ab!e.

8arious additiona! functions support $ou in mode!in# and eecutin# an ana!$sis

process, as we!! as in interpretin# the ana!$sis resu!ts. &ore information' -hec2in#

Data

The fo!!owin# #ure shows the various steps in the (na!$sis )rocess Desi#ner'

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7irst se!ect a data tar#et that contains the re*uired data. This data is then prepared

and transformed. The transformed data is saved in a %I object or in another s$stem.

7or ana!$sis, $ou can disp!a$ the data in a *uer$ in the %usiness +p!orer.

&ore information' &ode!in# an (na!$sis )rocess

Use in )rocess -hains

/ou can inte#rate an ana!$sis process into a process chain usin# the process t$pe

(%() )ro#ram. To do this, choose the (%() report 4S(5>)4O-+SS>+?+-UT+.

8ersionin#

(na!$sis processes are inte#rated into the versionin# concept active, inactive

version, content version, and content de!iver$".

Transport -onnection

(na!$sis processes are connected to the %I transport s$stem as T6O@O objects. 7or

more information, see Transportin# %I Objects.

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