Introduction Data Vault
Historical development Business Intelligence
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1950
1960
1970
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1980
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1990
2000
2010
Turing : First computers
Codd : 3NF
Management Information Systems (MIS)
Executive Information Systems (EIS)
Kimball : Dimensional Modeling Kimball
Inmon : Corporate Information Factory
Enterprise Datawarehousing
Linstedt : Datavault
Ronstad : Anchor Modeling
Challenges Classical Datawarehouse
• Time-to-Build
– Complexity, High Failure Rates
• Lack of Agility
– Expensive and Extensive re-engineering required to adapt
• No auditability
– Lack of tracebility, accountability and compliance
• Departmental scope
– No enterprise view, inability to effectively integrate disparate
systems
• Duplicate efforts and Spread Marts
Dan Lindstedt - Founder
Data Vault
•
”The Data Vault is a detail oriented, historical tracking
and uniquely linked set of normalized tables that
support one or more functional areas of business. It is
a hybrid approach encompassing the best of breed
between 3rd normal form (3NF) and star schema. The
design is flexible, scalable, consistent and adaptable
to the needs of the enterprise..”
Dan Linstedt
•
“The Data Vault is a data modelling approach and
methodology that is specifically tuned to optimize your
Enterprise Datawarehouse initiatives.”
Hans Hultgren
The DWH Guru’s opinion
“The Data Vault is the optimal choice for modeling the
Enterprise Datawaerehouse in the DW 2.0 framework.”
Bill Inmon, June 2007
Datavault application in Netherlands
Datavault – 3 Building blocks
• Hub : Identification
– Unique collection of Business Keys
– Business Key: identity of an enterprise entity
•
Link : Relationship
– Unique collection of associations between
two or more Business Keys
– Unit Of Work (grain), transactions and events
• Satellite : Description
– Adds context to a Hub orLink
– Timebound (System Date/Time !)
Sample model
•
Identification
Relation
Description
Datavault modelled Datawarehouse Architecture
Source 1
Star 1
Star 6
Star 2
Star 7
Star 3
Star 8
Source ..
Star 4
Star 9
Source x
Star 5
Star x
Source 2
Data
Vault
EDW
Source 3
Error
Marts
(Operational)
Reports
Business
Data
Vault
Example
Data Vault model
Airline
#IATA Code
Airline
Aircraft
#RegNr
Aircraft Airline
Product
Hierarchy
Product Sale
Aircraft
Sale Flight
Product
Product
#ProductCode
Product
Sale
Sale
#TrxID
Product Shop
Flight
#FlightNr
#ScheduledDate
#ArrDepIndicator
Flight Gate Ramp
Sale
Product Shop
Sale Shop
Shop
#ShopNo
Flight
Aircraft Flight
Sale Shop
Gate / Ramp
#GateRampCode
Lounge
#LoungeCode
Gate / Ramp
Shop Lounge
Shop
Shop Lounge
Lounge
Gate Lounge
Added value of Datavault modelling
• Build Incrementally
– Think Big Start Small
• Scale to Infinity
– Insert only
• Auditability
– Tracebility of the data and it’s history, versioning of data
• Absorb all data all of the time
– Store RAW data, Independent Loads, Lazy Updates, Single Point of
Facts
• Adapts to new sources easily
– No need for re-engineering when new sources need to be integrated
• Need for Operational Business Intelligence
– Real Time Loading (SOA), Real Time Reporting and data maintenance
Build a flexible foundation with us!
DWH / Datavault
implementation
Semantic layer
Implementation
Develop dashboards
& reports
Data Quality
Management
Data Integration
Build an Analytics
capability
Self-service BI
Implementation
Big data POC’s
14
Datavault implementation services & tools
• Datavault Education
– By certified Datavault engineers
• Datavault Implementation consultancy
– Assist you in creating your Datavault model
• Datavault Implementation Tools
– Model driven Datawarehouse ETL code generation