Kishore Jaladi DW

7/25/2019 Kishore Jaladi DW

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Data Warehousing: Data

Models and OLAPoperationsBy

Kishore [email protected]!


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"opis #o$ered%. &nderstanding the ter! 'Data Warehousing(

). "hree*tier Deision +upport +yste!s

,. Approahes to OLAP ser$ers

-. Multi*di!ensional data !odel

. /OLAP

0. MOLAP

1. 2OLAP

3. Whih to hoose: #o!pare and #ontrast

4. #onlusion


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&nderstanding the ter! DataWarehousing Data Warehouse:

The term Data Warehouse was coined by Bill Inmon in 1990, whichhe defined in the following way: ! warehouse is a sub"ect#oriented,integrated, time#$ariant and non#$olatile collection of data in su%%ortof management&s decision ma'ing %rocess( )e defined the terms inthe sentence as follows:

Subject Oriented:

Data that gi$es information about a %articular sub"ect instead ofabout a com%any&s ongoing o%erations(

Integrated:Data that is gathered into the data warehouse from a $ariety ofsources and merged into a coherent whole(

Time-variant:

!ll data in the data warehouse is identified with a %articular time%eriod(

Non-volatileData is stable in a data warehouse( *ore data is added but data isne$er remo$ed( This enables management to gain a consistent

%icture of the business(


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Data Warehouse

Arhiteture


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Other i!portant

ter!inology5 6nterprise Data 7arehouseollets all in8or!ation a9out su9jetscustomers,products,sales,assets, personnel; that span theentire organieuti$e? !anager? analyst; !ake 8aster 9etter deisions

5 Online Analytial Proessing OLAP;an ele!ent o8 deision support syste!s D++;


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"hree*"ier Deision +upport+yste!s

5 Warehouse data9ase ser$er Al!ost al7ays a relational DBM+? rarely at Cles

5 OLAP ser$ers /elational OLAP /OLAP;: e>tended relational

DBM+ that !aps operations on !ultidi!ensionaldata to standard relational operators

Multidi!ensional OLAP MOLAP;: speial*purposeser$er that diretly i!ple!ents !ultidi!ensional

data and operations5 #lients uery and reporting tools Analysis tools

Data !ining tools


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"he #o!plete Deision +upport+yste!

Information Sources Data Warehouse

Server

(Tier 1)

OLAP Servers

(Tier 2)

Clients

(Tier 3)

Oerational

D!"s

Semistructure#

Sources

extract

transform

load

refresh

etc.

Data $arts

Data

Warehouse

e%&%' $OLAP

e%&%' OLAP

serve

OLAP

uer*+eortin&

Data $inin&

serve

serve


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Approahes to OLAP+er$ers"hree possi9ilities 8or OLAP ser$ers%; /elational OLAP /OLAP;

/elational and speiali


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"he Multi*Di!ensional DataModel

Sales by product line over the past six monthsSales by store between 1990 and 1995

Pro# Co#e Time Co#e Store Co#e Sales t*

Store Info

Pro#uct Info

Time Info

% % %

,umerical $easures-e* columns .oinin& fact ta/le

to #imension ta/les

Fact table for

measures

Dimension tables


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/OLAP: Di!ensional Modeling &sing/elational DBM+

5 +peial she!a design: star, snowfake

5 +peial inde>es: 9it!ap? !ulti*ta9le join

5 Pro$en tehnology relational !odel? DBM+;?tend to outper8or! speiali


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+tar +he!a in /DBM+;


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+tar +he!a 6>a!ple


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"he '#lassi( +tar +he!a

A single 8at ta9le? 7ithdetail and su!!ary data

Eat ta9le pri!ary keyhas only one key olu!nper di!ension

6ah key is generated 6ah di!ension is a

single ta9le? highly de*nor!ali


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Star Schema

ith Samle

Data


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"he '+no7ake( +he!a

STORE KEYStoreDimension

Store Descri#tionCityStateDistrict ID

Reion%IDReional Mr$

District%ID

District Desc$Reion%ID

Reion%ID

Reion Desc$Reional Mr$

STORE KEY

PRODUCT KEY

PERIOD KEY

DollarsUnitsPrice

Store Fact Ta"le


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Aggregation in a +ingle Eat "a9le

Dra+"ac,s: +u!!ary data in the 8at ta9le yields poorer per8or!ane 8or

su!!ary le$els? huge di!ension ta9les a pro9le!

PERIOD KEY

Store Dimension Time Dimension

Product Dimension

STORE KEY

PRODUCT KEY

PERIOD KEY

DollarsUnitsPrice

Period Desc

YearQuarterMonthDayCurrent FlaResolutionSe!uence

Fact Ta"le

PRODUCT KEY

Store Descri#tionCity

StateDistrict IDDistrict Desc$Reion%IDReion Desc$Reional Mr$&e'el

Product Desc$(randColorSi)e

Manu*acturer&e'el

STORE KEY


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PERIOD KEY

Store Dimension Time Dimension

Product Dimension

STORE KEY

PRODUCT KEY

PERIOD KEY

DollarsUnitsPrice

Period DescYearQuarterMonth

DayCurrent FlaSe!uence

Fact Ta"le

PRODUCT KEY

Store Descri#tionCityStateDistrict IDDistrict Desc$Reion%IDReion Desc$Reional Mr$

Product Desc$(randColorSi)eManu*acturer

STORE KEY

"he 'Eat #onstellation(+he!a

DollarsUnitsPrice

District Fact Ta"le

District%IDPRODUCT%KE

YPERIOD%KEY

DollarsUnitsPrice

Reion Fact Ta"le

Reion%ID

PRODUCT%KEYPERIOD%KEY


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"he

Aggregations using'+no7ake( +he!a and

Multiple Eat "a9les

5 Fo L6G6L in di!ension ta9les5 Di!ension ta9les are nor!ali


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Aggregation #ontd IAggregation #ontd I

Advantage:Advantage:!est erformance hen 7ueries involve a&&re&ation!est erformance hen 7ueries involve a&&re&ation

Disadvantage:Disadvantage:Comlicate# maintenance an# meta#ata' e8losion in the num/erComlicate# maintenance an# meta#ata' e8losion in the num/er

of ta/les in the #ata/aseof ta/les in the #ata/ase

STORE KEY

Store Dimension

Store Descri#tion

City

State

District ID

District Desc$

Reion%ID

Reion Desc$Reional Mr$

District%ID

District Desc$

Reion%ID

Reion%ID

Reion Desc$

Reional Mr$

STORE KEY

PRODUCT KEY

PERIOD KEY

Dollars

Units

Price

Store Fact Ta"le

DollarsUnits

Price

District Fact Ta"le

District_ID

PRODUCT_KEY

PERIOD_KEY DollarsUnitsPrice

ReionFact Ta"le

Region_ID

PRODUCT_KEY

PERIOD_KEY


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Aggregates

Add up amounts for day 1In SQL: SELECT sum(amt F!"# SALE

$%E!E date & 1

'1


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Aggregates

Add up amounts by dayIn SQL: SELECT date sum(amt F!"# SALE

)!"*+ ,- date

ans date sum

1 '1

. /'

sale prodId storeId date amtp1 s1 1 1.p. s1 1 11p1 s0 1 2p. s. 1 'p1 s1 . //

p1 s. . /


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Another 6>a!pleAdd up amounts by day productSQL: SELECT prodid date sum(amt F!"# SALE

)!"*+ ,- date prodId

sale prodId date amt

p1 1 3.

p. 1 14

p1 . /'

drill5do6n

rollup

sale prodId storeId date amtp1 s1 1 1.p. s1 1 11p1 s0 1 2p. s. 1 'p1 s1 . //p1 s. . /


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Points to 9e notied a9out /OLAP

5 DeCnes o!ple>? !ulti*di!ensional data 7ithsi!ple !odel

5 /edues the nu!9er o8 joins a uery has toproess

5 Allo7s the data 7arehouse to e$ol$e 7ith rel.lo7 !aintenane

5 #an ontain 9oth detailed and su!!ari


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MOLAP: Di!ensional Modeling&sing the Multi Di!ensional Model

5MDDB: a speial*purpose data !odel5Eats stored in !ulti*di!ensional

arrays

5Di!ensions used to inde> array

5+o!eti!es on top o8 relational DB

5Produts Pilot? Ar9or 6ss9ase? entia


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"he MOLAP #u9e

sale prodId storeId amtp1 s1 1.p. s1 11p1 s0 2p. s. '

s1 s2 sp1 1. 2p. 11 '

Fact table 7ie6:#ulti5dimensional cube:

dimensions & .


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,*D #u9e,*D #u9e

dimensions & 0dimensions & 0

#ulti5dimensional cube:#ulti5dimensional cube:Fact table 7ie6:Fact table 7ie6:

sale prodId storeId date amt

p1 s1 1 1.p. s1 1 11p1 s0 1 2p. s. 1 'p1 s1 . //p1 s. . /

da! 2da! 2s1 s2 s

p1 // /p. s1 s2 s

p1 1. 2p. 11 '

da! 1da! 1


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6>a!ple6>a!ple

Stor

e

Stor

e

Product

Produc

t

TimeTime

$ T W Th 9 S S$ T W Th 9 S S

:uice:uice

$il5$il5

Co5eCo5e

CreamCream

SoaSoa

!rea#!rea#

,;,;

S9S9

LALA

1?>?

3232

1212

>?>?

>? units of /rea# sol# in LA on $>? units of /rea# sol# in LA on $

Dimensions:Dimensions:

Time' Pro#uct' StoreTime' Pro#uct' Store

Attributes:Attributes:

Pro#uct (uc' rice' @)Pro#uct (uc' rice' @)

Store @Store @@@

Hierarchies:Hierarchies:

Pro#uctPro#uct !ran#!ran# @@

Da*Da* Wee5Wee5 uarteruarter

StoreStore e&ione&ion Countr*Countr*rollu to ee5rollu to ee5

rollu to /ran#rollu to /ran#

rollu to re&ionrollu to re&ion


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#u9e Aggregation: /oll*up

da! 2s1 s2 s

p1 // /p. s1 s2 s

p1 1. 2

p. 11 '

da! 1

s1 s2 sp1 3 / 2

p. 11 '

s1 s2 s

sum 38 1. 2

sum

p1 112

p. 14

1.4

" " "

drill5do6n

rollup

E9ample: computin sums


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#u9e Operators 8or /oll*up#u9e Operators 8or /oll*up

da! 2da! 2s1 s2 s

p1 // /p. s1 s2 s

p1 1. 2

p. 11 '

da! 1da! 1

s1 s2 sp1 3 / 2

p. 11 '

s1 s2 s

sum 38 1. 2

sum

p1 112

p. 14

1.41.4

" " "" " "

sale#s1$%$%&sale#s1$%$%&

sale#%$%$%&sale#%$%$%&sale#s2$p2$%&sale#s2$p2$%&


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s1 s2 s ;

p1 3 / 2 112p. 11 ' 14; 38 1. 2 1.4

6>tended #u9e6>tended #u9e

da! 2da! 2 s1 s2 s ;p1 // / /'p.; // / /'s1 s2 s ;

p1 1. 2 3.p. 11 ' 14

; .0 ' 2 '1

da! 1da! 1

%%

sale#%$p2$%&sale#%$p2$%&


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Aggregation &sing

2ierarhies

region ' region (

p1 3 /

p. 11 '

store

reion

country

(store s1 in !eion A

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Kishore Jaladi DW