Take the first review of Inmon’s model compared to Kimball’s model (original links do not work any more) – both of those guys are the pioneers of the information architecture and a very well-known influencers from the early 1980’s.
Inmon: All corporate information, further processed, “one truth”. A central data warehouse. Data Warehouse including historical data – the Corporate Information Factory (CIF)
Inmon’s CIF includes the data from the operational systems, data transfer and processing processes (ETL), data warehouse, and the establishment of the special needs of smaller data warehouses (data marts). Data Marts are always the data warehouse data, “one truth”, never data from anywhere else. These are the special needs of different departments and processes, analytical solutions, etc.
Data integration is time-consuming step. This work requires the utmost diligence, discipline. The entire organization is committed to information architecture.
CIF: A Data Warehouse should be located in a normalized relational database format. History-containing structures may be permitted to also be “de-normalized”, at least to some extent.
The work is a long-term, construction will last a long time, but in return is expected to be and should be long-lasting and reliable data architecture.
Kimball’s model is considered to represent “opposite” view of how the company will design and build architecture. Kimball’s model is also called “dimensional” model (stars, snowflakes).
In this approach, dimensional data structures (data marts) come directly from the organization’s applications. The same information can be transferred to more than just a single data mart, depending on the function of the individual models.
Interest and the criterion of this approach is the speed of development. Analytical and reporting needs can be quickly implemented when there is not a target to design and build the whole enterprise on a common data repository. Inmon other hand says that Kimball’s model used for the company there is no “one truth”
Kimball’s Read More