Inmon vs Kimball – Architecture Comparison and Data Valut Architect

1) A good slide show for reference:
http://www.slideshare.net/kgraziano/introduction-to-data-vault-modeling#btnNext

Inmon states that the data warehouse is:

Subject-oriented

The data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together.

Non-volatile

Data in the data warehouse are never over-written or deleted — once committed, the data are static, read-only, and retained for future reporting.

Integrated

The data warehouse contains data from most or all of an organization's operational systems and these data are made consistent.

Time-variant

For An operational system, the stored data contains the current value.

Kimball: DW is where we publish used data.

Data Valut by Dan:
Hub: Business Key
Link: represent 3nf many to many relation for Business key
Satellite: description for business key

2)
From:

http://oracledwbi.wordpress.com/2010/03/25/inmon-vs-kimball-architecture-comparison/

	Inmon – Hub & Spoke	Kimball – Bus Architecture
Methodology & Architecture
Overall approach	Top-down	Bottom-up
Architectural structure	Enterprise wide (atomic) data warehouse “feeds” departmental databases	Data marts model a single business process; enterprise consistency achieved through data bus and conformed dimensions
Complexity of the method	Quite complex	Fairly simple
Comparison with established development methodologies	Derived from the spiral methodology	Four-step process; a departure from RDBMS methods
Discussion of physical design	Fairly thorough	Fairly light
Scalability	Growing scope and changing requirements are critical	Need to adapt to highly volatile needs within a limited scope
Application	Strategic solution	Tactical solution
Information interdependence	Supported	Supported
Deployment cost	Higher start-up costs, with lower subsequent project development costs	Lower start-up costs, with each subsequent project costing about the same
Time to deploy	Longer start-up time	Quick start-up time
Development skills required	Larger team(s) of specialists	Small teams of generalists
Persistency of data	High rate of change from source systems	Source systems are relatively stable
Data modelling
Data orientation	Subject or data-driven	Process oriented
Tools	Traditional (ERDs, DISs)	Dimensional modelling
End-user accessibility	Low	High
Philosophy
Primary audience	IT professionals	End users
Place in the organisation	Corporate wide facility	Growth subject to departmental demand
Objective	Deliver a sound technical solution based on proven database methods and technologies	Deliver a solution that makes it easy for end users to directly query the data and still get reasonable response times