kishore jaladi dw
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Data Warehousing: Data
Models and OLAPoperationsBy
Kishore Jaladikishorejaladi@yahoo.o!
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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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