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  Data Warehousing: A Perspective by Hemant Kirpekar10/18/201 Data Warehousing: A Perspective by Hemant Kirpekar  Introduction !he ee# $or proper un#erstan#ing o$ Data Warehousing%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2 !he Key &ssues%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3 !he De$inition o$ a Data Warehouse%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3 !he 'i$ecyc(e o$ a Data Warehouse%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4 !he )oa(s o$ a Data Warehouse%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 5 Why Data Warehousing is different from OLTP.................................................6E! ode#ing $s Dimension Ta%#es....................................................................&T'o (amp#e Data Warehouse Designs Designing a Pro#uct*+riente# Data Warehouse%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 10 Designing a ,ustomer*+riente# Data Warehouse%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 14 echanics of the Design &ntervie-ing .n#*sers an# DAs%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 19 Assemb(ing the team%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 19 ,hoosing Har#-are/o$t-are p(at$orms%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 20 Han#(ing Aggregates%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 20 erver*i#e activities%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 21 ,(ient*i#e activities%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 22 )onc#usions.........................................................................................................*+A )hec,#ist for an Idea# Data Warehouse.........................................................*- 1  Data Warehousing: A Perspective by Hemant Kirpekar 10/18/201 Introduction The need for proper understanding of Data Warehousing  !he $o((o-ing is an etract $rom 3Kno-(e#ge Asset 4anagement an# ,orporate 4emory3 a White Paper to be pub(ishe# on the WWW possib(y via the Hispacom site in the thir# -eek o$ August 1556%%%%%%Data Warehousing may -e(( (everage the rising ti#e techno(ogies that everyone -i(( -ant or nee#7 ho-ever the current tren# in Data Warehousing marketing (eaves a (ot to be #esire#%&n many organiations there sti(( eists an enormous #ivi#e that separates &n$ormation !echno(ogy an# a managers nee# $or Kno-(e#ge an# &n$ormation% &t is common currency that there is a -ho(e host o$ avai(ab(e too(s an# techni9ues $or (ocating7 scrubbing7 sorting7 storing7 structuring7 #ocumenting7  processing an# presenting in$ormation% n$ortunate(y7 too(s are tangib(e an# business in$ormation an# kno-(e#ge are not7 so they ten# to get con$use#%o -hy #o -e sti(( have this con$usion ;irst consi#er ho- certain companies market Data Warehousing% !here are companies that se(( #atabase techno(ogies7 other companies that se(( the p(at$orms <ostensib(y consisting o$ an 4PP or 4P architecture=7 some se(( technica( ,onsu(tancy services7 others meta*#ata too(s an# services7 $ina((y there are the business ,onsu(tancy services an# the systems integrators * each an# everyone -ith their o-n particu(ar $ocus on the critica( $actors in the success o$ Data Warehousing  pro>ects%&n the main7 most ?D4 ven#ors seem to see Data Warehouse pro>ects as a cha((enge to provi#e greater  per$ormance7 greater capacity an# greater #ivergence% With this ecuse7 most ?D4 pro#ucts carry $unctiona(ity that make them about as tru(y 3open3 as a  &@A, 50/07 i%e% o stan#ar#s $or @ie- Partitioning7 it 4appe# &n#eing7 Histograms7 +b>ect Partitioning7 B' 9uery #ecomposition or B' eva(uation strategies etc% !his ho-ever is not rea((y the important issue7 the rea( issue is that some ven#orsse(( Data Warehousing as i$ it >ust provi#e# a big #umping groun# $or massive amounts o$ #ata -ith -hichusers are a((o-e# to #o anything they (ike7 -hi(st at the same time $reeing up +perationa( ystems $rom the nee# to support en#*user in$ormationa( re9uirements% ome har#-are ven#ors have a simi(ar approach7 i%e% a Data Warehouse p(at$orm must inherent(y have a (ot o$ #isks7 a (ot o$ memory an# a (ot o$ ,Ps% Ho-ever7 one o$ the most success$u( Data Warehouse  pro>ects have -orke# on use# ,+4PAB har#-are7 -hich provi#es an ece((ent cost/bene$it ratio% ome !echnica( ,onsu(tancy ervices provi#ers ten# to #-e(( on the per$ormance aspects o$ Data Warehousing% !hey see Data Warehousing as a technica( cha((enge7 rather than a business opportunity7 butthe biggest per$ormance payo$$s -i(( be brought about -hen there is a $u(( un#erstan#ing o$ ho- the user -ishes to use the in$ormation% 2  Data Warehousing: A Perspective by Hemant Kirpekar 10/18/201 The Key Issues +rganiations are s-imming in #ata% Ho-ever7 most -i(( have to create ne- #ata -ith improve# 9ua(ity7 to meet strategic business p(anning re9uirements%o:Ho- shou(# & p(an $or the mass o$ en# user in$ormation #eman#What ven#ors an# too(s -i(( emerge to he(p & bui(# an# maintain a #ata -arehouse architectureWhat strategies can users #ep(oy to #eve(op a success$u( #ata -arehouse architecture What techno(ogy breakthroughs -i(( occur to empo-er kno-(e#ge -orkers an# re#uce operationa( #ata access re9uirements!hese are some o$ the key 9uestions out(ine# by the )artner )roup in their 155C report on Data Warehousing%& -i(( try to ans-er some o$ these 9uestions in this report% The Definition a Data Warehouse A Data Warehouse is a:% sub>ect*oriente#% integrate#%time*variant% non*vo(ati(eco((ection o$ #ata in support o$ management #ecisions%<W%H% &nmon7 in 3ui(#ing a Data Warehouse7 Wi(ey 1556=!he #ata -arehouse is oriente# to the ma>or  subject areas o$ the corporation that have been #e$ine# in the #ata mo#e(% .amp(es o$ sub>ect areas are: customer7 pro#uct7 activity7 po(icy7 c(aim7 account% !he ma>or sub>ect areas en# up being physica((y imp(emente# as a series o$ re(ate# tab(es in the #ata -arehouse%  Personal Note: Could these be objects? No one to my knowledge has explored this possibility as yet. !he secon# sa(ient characteristic o$ the #ata -arehouse is that it is integrated.  !his is the most important aspect o$ a #ata -arehouse% !he #i$$erent #esign #ecisions that the app(ication #esigners have ma#e over the years sho- up in a thousan# #i$$erent -ays% )enera((y7 there is no app(ication consistency in enco#ing7 naming conventions7 physica( attributes7 measurements o$ attributes7 key structure an# physica( characteristics o$ the #ata% .ach app(ication has been most (ike(y been #esigne# in#epen#ent(y% As #ata is entere# into the #ata -arehouse7 inconsistencies o$ the app(ication (eve( are un#one%!he thir# sa(ient characteristic o$ the #ata -arehouse is that it is time-variant.  A C to 10 year time horiono$ #ata is norma( $or the #ata -arehouse% Data Warehouse #ata is a sophisticate# series o$ snapshots taken at one moment in time an# the key structure a(-ays contains some time e(ement%!he (ast important characteristic o$ the #ata -arehouse is that it is nonvolatile. n(ike operationa( #ata -arehouse #ata is (oa#e# en masse an# is then accesse#% p#ate o$ the #ata #oes not occur in the #ata -arehouse environment% 3  Data Warehousing: A Perspective by Hemant Kirpekar 10/18/201 The lifecycle of the Data Warehouse Data $(o-s into the #ata -arehouse $rom the operationa( environment% sua((y a signi$icant amount o$ trans$ormation o$ #ata occurs at the passage $rom the operationa( (eve( to the #ata -arehouse (eve(% +nce the #ata ages7 it passes $rom current #etai( to o(#er #etai(% As the #ata is summarie#7 it passes $rom current #etai( to (ight(y summarie# #ata an# then onto summarie# #ata%At some point in time #ata is purge# $rom the -arehouse% !here are severa( -ays in -hich this can be ma#e to happen:% Data is a##e# to a ro((ing summary $i(e -here the #etai( is (ost%% Data is trans$erre# to a bu(k me#ium $rom a high*per$ormance me#ium such as DAD%% Data is trans$erre# $rom one (eve( o$ the architecture to another%% Data is actua((y purge# $rom the system at the DAs re9uest%!he $o((o-ing #iagram is $rom 3ui(#ing a Data Warehouse3 2n# .#7 by W%H% &nmon7 Wi(ey 56 high(y summarie#(ight(y summarie#<#ata mart=month(y sa(es by pro#uct (ine <E81 * E52=-k(y sa(es by subpro#uct (ine<E8 * E52=sa(es #etai( <1550 * 1551=sa(es #etai( <E8 * E85=o(# #etai(operationa(trans$ormationcurrent#etai(meta#ata Structure of a Data Warehouse 4

Cm Publi 3666

Jul 23, 2017
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