11g Data Warehousing

8/18/2019 11g Data Warehousing

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Oracle Data Warehouse Strategic Update

Ray Roccaforte


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The following is intended to outline our general

product direction. It is intended for information

purposes only, and may not be incorporated into any

contract. It is not a commitment to delier any

material, code, or functionality, and should not berelied upon in ma!ing purchasing decisions.

The deelopment, release, and timing of any

features or functionality described for "racle#s

products remains at the sole discretion of "racle.


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!our Pillars of Oracle"s DW Strategy

$ "racle as a %ystems Proider& Database 'achine

$ 'a(imum )eerage of 'assie *ardware +apabilities

$ mbedded Analytics

$ "racle as a DW %olutions Proider&$ (adata Intelligent Warehouse for Industries


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"racle as a %ystems Proider& Database 'achine


$ mbedded Analytics



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-

Sun Oracle Data#ase $achine

$ Database 'achine eliminates the comple(ityof deploying database systems

$ alanced configuration$ (treme performance and integrated analytics out of the bo(

$ Deploys in days rather than months$ Pre/built, tested, standard, supportable configuration

$ Includes *ardware Installation 0 %ite Planning

$ %oftware +onfiguration


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%&adata Hard'are (rchitecture

Data#ase )rid

$ 1 co*pute serers2345

Storage )rid

$ 36 storage serers 2745

$ 388 T High Speed dis!,or 99- T High +apacity dis!

$, -. P+I !lash

$ Data *irrored acrossstorage ser/ers

Scalea#le )rid of industry standard ser/ers for +o*pute and

Storage

Infini.and et'or1

$ Redundant 0)#3s s'itches

$ Unified ser/er 4 storage net

: 7838 "racle +orporation5


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e' %&adata 6278

$ ;ull Rac! Database 'achine for large deployments

$ +onsolidation, or )arge ")TP and DW wor!loads$ +omplements e(isting


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'a(imum )eerage of 'assie *ardware +apabilities

$ mbedded Analytics



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%&adata Hy#rid +olu*nar +o*pression

$ Data is grouped by columnand then compressed

$ :uery $ode for data

warehousing$ "ptimi@ed for speed$ 06 co*pression typical$ %cans improe proportionally

$ (rchi/al $ode for

infreuently accessed data$ "ptimi@ed to reduce space

$ 3B< compression is typical

$ 4p to B8< for some data


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$ %un "racle Database 'achine proides semiconductorcache hierarchy

Sun Oracle Data#ase $achine;

!aster ra' scans

%&adata dis1s

00-. ra' dis1 capacity

2 ).3sec

%&adata S*art !lash +ache

,-. ra' capacity up to ,0-. user data

,0 ).3sec

Data#ase DR($ +ache

00). ra' capacity up to -. user data

00 ).3sec


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In7$e*ory Parallel %&ecution

$ Parallel uery processing across tables which are

cached in memory

$ *ow it wor!s&

$ "racle determines appropriate tables for caching$ Tables are distributed across the buffer cache in all nodes

$ During ueries, each node reads its local portion of the table

$ ;ull parallel bandwidth accessing data in memory

$ ?ot eery database obCects in a uery must be in memory to

leerage in/memory access

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%&adata !lash +ache

$ (adata has B T of ;lash& B- ;lash +ards aoid dis! controller bottlenec!s

$ Automatically caches freuently/accessed hot# data in flash storage

$ Intelligently manages ;lash& >ies speed of ;lash at cost of dis!

$ (adata ;lash +ache achiees& O/er *illion IO3sec fro* S:= 8?@A Su#7*illisecond response ti*es 'ith ,0 ).3sec Buery throughput

Infreuently

4sed Data

;reuently4sed Data


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Scanning fro* Dis1; %&adata S*art Scan

$ (adata storage serers implement data

intensie processing in storage$ Row filtering based on EwhereF predicate

$ +olumn filtering

$ Goin filtering

$ Incremental bac!up filtering

$ Scans on Hy#rid +olu*nar +o*pressed data

$ Scans on encrypted data

$ Data $ining *odel scoring

$ 38( reduction in data sent to D serers

is common

$ ?o application changes needed$ Processing is automatic and transparent

$ en if cell or dis! fails during a uery


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mbedded Analytics



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Oracle In7Data#ase (nalytics

Oracle In7Data#ase (nalytics

$ Analytics ealuated EcloseF to the data

$ Integrated

$ %ecure

Oracle Data $ining; Unco/er 4 Predict

$ Adanced Predictie

Analytics

$ Adanced Algorithms

$ mbedded andIntegrated

Oracle O=(P; (nalyCe 4 Su**ariCe

$ %marter, ;aster I

$ Improed Deeloper

Productiity

$ mbedded andIntegrated


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In7Data#ase Data $ining%&adata S*art Scan

$ 'odel scoring

performed

in the (adata

%torage %erer

$ Dramatic 27/B


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%&adata Integration 'ith S(S

$ %core %A% 'odels in (adata

$ Import %A% models 2ia P'')5 to

"racle Data 'ining

$ )ogistic and 'ultiple Regressionmodels aailable today

$ *igh/performance scoring in

(adata

$ %core directly against database tables

$ 'odel scoring is e(ecuted in storage

tier using natie "racle D' capabilities


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Oracle O=(P

$ Rapid Deelopment of rich dimensional analytics$ nhances the analytic content of usiness intelligence application

$ (cellent uery performance for ad/hoc H unpredictable uery

$ ;ast, incremental updates of data sets

$ mbedded in the "racle database instance and storage.$ %afe, secure and manageable

$ enefits from and enhances (adata architecture$ Automatically gets full benefit of Database Machine Flash Cache

$ ")AP uery IH" tends to be high olume, random reads


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Oracle O=(P g Rich +alculations Deli/ered to S:=7.ased .I -ools


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O=(P $D6 dri/er fro* Si*#a for $S %&cel

: 788 "racle +orporation

$D6 Parser $D6 Pro/ider %ngine

S:= )enerator

S:= :uery of +u#e

%&cel $D6 :uery

$D6 Pro/ider forOracle O=(P

%&cel 200

(cel 7889


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$ mbedded Analytics

"racle as a DW %olutions Proider&$ (adata Intelligent Warehouse for Industries


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$ Intelligence J Industry/specific data models

J Pac!aged adanced analytics

$ ... with (treme Performance

J Improe uery performance 38/388(

with (adata

$ K at a )ower +ost

J %implify your infrastructure

$ K for ;ast Results

J Gumpstart deelopment / delieralue uic!ly

J Automatically e(ploit "racle

performance and analytic capabilities

%&adata Intelligent Warehouse for Industries( +o*plete Data Warehouse Solution for Industries


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%&adata Intelligent Warehouse Solution for IndustriesPac!aged (perience and Technology

$ )eerage enterprise/wide data model forindustries

$ >ain insights using prepac!agedadanced analytics

.usiness Insight

$ Improe uery performance 38 /388(

$ >row solution to irtually any scale%&tre*ePerfor*ance

$ Gumpstart deelopment and delier alueuic!ly

$ )ower ris!s

!ast-i*e7to7Ealue


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%&adata Intelligent Warehouse for RetailPac1aged %&perience and -echnology

+opyright : 7838, "racle and H or its affiliates. All rights resered.

$ Improe uery performance38 /388(

$ >row solution to irtually any scale

%&tre*ePerfor*ance

$ Gumpstart deelopment and delieralue uic!ly

$ )ower ris!s

!ast Results

$ )eerage enterprise data model forretail


.usinessInsight

$ )eerage enterprise data model forretail


.usinessInsight


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%&adata Intelligent Warehouse for Retail+onsolidated Eie' of the %ntire .usiness

Oracle RetailData $odel

$ POS

$ Order $g*t$ Wor1force

Scheduling

Sell Side$ PO $g*t

$ Warehouse$ Pricing$ +osting

.uy Side

$ HR$ !inance$ Sales !orecasting

Inside

$ Partners$ Suppliers$ Distri#utors

Outside

200 Industry7specific$easures 4 ?PIs


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Interacti/e Dash#oards and Reportsmpowering nd 4sers

Anticipate out/of/stoc! usingstatistical forecasts

%&a*ple (nalytics; Retail

Associate products using a mar!etbas!et analysis

Analy@e store traffic and shopperconersion rates by time of day

4nderstand effectieness of promotions by store

Analy@e comparatie storeperformance oer time

2F+opyright : 7838, "racle and H or its affiliates. All rights resered.


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%&adata Intelligent Warehouse for +o**unications

%ame idea, but specific to +ommunications industry


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Interacti/e Dash#oards and Reportsmpowering nd 4sers

Identify customers that are li!ely tochurn

%&a*ple (nalyses; +o**unications

'onitor dropped call rate oer time todetect failing networ! elements

+ompare billed and actual +DRs to identifybilling errors

Analy@e sales growth oer time byproduct and organi@ation

G+opyright : 7838, "racle and H or its affiliates. All rights resered.


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%&adata Intelligent WarehousePac1aged %&perience and -echnology




%&tre*ePerfor*ance

$ Gumpstart deelopment and delier

alue uic!ly

$ )ower ris!s

!ast Results

$ )eerage enterprise data model forIndustries


.usinessInsight


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%&adata Intelligent WarehousePac1aged %&perience and -echnology




%&tre*ePerfor*ance

$ Gumpstart deelopment and delier

alue uic!ly

$ )ower ris!s

!ast Results

$ )eerage enterprise data model forIndustries


.usinessInsight


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+usto* Solutions?ey I*ple*entation -as1s

+hallenges;

$ Design a data *odel that satisfies e&isting and future reBuire*ents

$ %*ploy a di/erse s1ill set

$ IntegrateA i*ple*entA ad*inister and tune disparate technologies


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%&adata Intelligent Warehouse.est Practice I*ple*entation

+o*plete !ast Results =o'er Ris1

.enefits;

$ UtiliCe an enterprise data *odel for retail

$ =e/erage your e&isting Oracle e&pertise

$ I*ple*ented using Oracle data 'arehousing #est practices


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!ast ResultsRetail Perfor*ance $etrics and Insight out of the #o&

%peed to Value, %implified Deployment with predictable cost

.uild fro* Scratch 'ith

.est of .reed (pproachOracle %&adata

Intelligent Warehouse

'ee1s or *onths*onths or years

SiCing and +onfiguration

Define $etrics 4 Dash#oards

-raining 4 Roll7out

Data Integration

Data Integration

SiCing and +onfiguration

(nalysis and Design Define $etrics 4 Dash#oards

(nalysis and Design

-raining 4 Roll7out

-raining 4 Roll7out


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%&adata Intelligent Warehouse Solution for IndustriesPac!aged (perience and Technology

.usiness Insight

%&tre*e Perfor*ance

!ast -i*e7to7Ealue


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Su**ary; Oracle"s DW Strategy



$ mbedded Analytics


11g Data Warehousing

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