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Introducing dwh 2010
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Introducing dwh 2010
1.
Introducing DATA
WAREHOUSING 2010 A tour of definition
2.
RienMatthijsse 1st computer: 1976
ICT: 1986 ICT freelancer: 1998 TGF’er: April 2009
3.
MONDAY
4.
Data Warehousing is
no Rocket science
5.
Sir Isaac Newton
6.
Physics E=mc²
7.
Thermodynamics
8.
Astronomy
9.
10.
The position
of a Data Warehouse
11.
Decisions Decisions Decisions
12.
Business Performance Indicators
13.
Organization structure
14.
Information Provisioning
15.
Integration
16.
Centralized archive
17.
Solution: Data Warehouse
18.
So what’s so
different?
19.
Capturing business processes
20.
Current state
21.
Twinkling
22.
Storage & Retrieval
23.
History
24.
Batch oriented
25.
To BI
or Data Warehouse
26.
Business Intelligence
27.
Data Warehouse
28.
Business Intelligence Data
warehouse ETL Hardware RDBMS
29.
Talk the talk
@ the right place @ the right person
30.
Data as
Information
31.
Presentation
32.
Star models
33.
Ralph Kimball
34.
Ralph Kimball
35.
Product Group Product
Customer CustomerGroup Product Line Customer Variety Day Period Week Month Year Per Product Group Howmuch Revenue Qty Ordered ….. Country Country In the last month Per Country
36.
Multi- Dimensional Data
modeling
37.
Historical correctness
38.
History
39.
Versioning through time
40.
Slow moving Type
2
41.
Data Warehouse
expert
42.
Expert
43.
44.
45.
Literature
46.
Terminology ‘Talk the
talk’
47.
Get involved!
48.
Sorry!
49.
Experience
50.
Thank you
51.
END OF PRESENTATION
Notes de l'éditeur
Blue MondayMeestdepressieve dag van het jaarOnzelaatstegoedevoornemensgaanverloren
DWH is omgeven met mysiekMaarDWHinggewooneen ICT discipline en GEEN ROCKETSCIENCE
De appelvaltnietver van de boomZwaartekracht is overalActie = reactie
NatuurkundeKracht = Massa * VersnellingSnelheid = Inisnelheid + versnelling * tijdIntegraalPlaats = init snelheid * tijd + ½ * versnelling * tijd
WarmteVerbrandingRaketmoteren
Plaatsbepaling in de ruimtePlanetenZwartegaten
Niet 1x DWH genoemd: dusDWHing <> RocketscienceGeen NewtonGeenmoeilijkeformulesGeenraketmoterenGeenplaneten en zoHihi
Waarom/waarvooreen DWH inzetten?
Business == beslissingennemenGoedebeslissingen == goederelevanteinformatie
Hoe neemthoger management beslissingen?Waarkomtinformatievandaan?Is deze correct?
Hoe komenzeaaninformatie?Bestaandesystemenleverennietaltijd de juisteVeelalvindenzeinformatiezelf en bewaren die in excel sheets of access databases
CorrectheidAccuraatheidTijdigheid
InformatieuitverschillendebronsystemenSamengebracht in 1 omgevingEeneenduidigheidVastgesteldedefinitie
Iedereenmaaktgebruik van 1 bronvoor businessinformatie
Zo’noplossingheeteen DWH
Vergelijking OLTP> DWH
OLTPregistreren data van business processenVeelgebruikers, tegelijkertijdWeinig resource beslag per gebruikerKorte/kleinetransacties
OTLP data over day-to-day businessGeen to beperkteopslag van historie: eenhuidige order is gekoppeldaan de huidigeinformatie van eenklant
De onderliggende database is continue in beweging, mutatiesvindenveelvuldigplaats
DWH focus:opslag en snelletoegangvoorgrote volumes voorrapportage/analyses
Archief: historie
Wijzigingenzijn batch georienteerd en vindenplaats op vasteverwerkingswindows.
Wanneerspreken we over BI en wanneer over DWHNu vaakverwarrendmn door de specialisten.
BI: het omgaan met informatie relevant bij de uitvoering van eenzekere business.
DWH: opslag en beschikbaarstellen van data
DWH is gereedschapkistvoor BI
Blackbox
Wanneerwordt data informatie?Hoe stel je gebruikers data alsinformatiebeschikbaar?
VeelalstermodellenEen semi genormaliseerdedatamodelvormDimensiesFacts: semi additief
Ooit in de begin jaren ‘90 bedacht door Ralph KimballGrondlegger
Dimensies:Facts:Eenduidigevertaling vaneen BQ SQL op eensterschema
Profi talk
DWH == historischecorrectheid
Profi talk
Parker is geen expert overnight gewordenJaren van oefenen
Geenwondermiddelbeschikbaar
Lees relevanteliteratuur en boeken
Baboushka talk
Zorgdat je betrokkenraaktbijeen DWH project
Data Warehousing for dummies bestaatniet