Dimensionality Modelling in top-down and bottom-up approach.

How are dimensional models used different in the two approaches to data warehousing?

I understand that a data warehouse created using the bottom-u开发者_开发知识库p approach has data marts as the building blocks of the data warehouse, and each data mart has it's own dimensional model. Is it the same for the top-down approach? Does Inmons method use dimensional models?

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Kimball's method uses (collection of) data-marts with a common "dimension bus" as a data-warehouse.

Inmon's method has a subject-oriented normalized structure as a warehouse, and then from that structure the data is exported to data-marts, which may (or may not) be star-shaped as Kimball's.

For very large warehouses, those two architectures converge -- at least become similar -- due to introduction of master-data management structure/storage in the Kimball-type architecture.

There is a white paper on Inmon's site called A Tale of Two Architectures which nicely summarizes the two approaches.

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Dimensional modelling is a design pattern sometimes used for Data Marts. It's not a very effective technique for complex Data Warehouse design due to the redundancy and in-built bias in dimensional models. Kimball's "bottom-up" approach attempts to sidestep the issue by referring to a collection of Data Marts as a "Data Warehouse" - an excuse that looks far less credible today than it did in the 1990s when Kimball first proposed it.

Inmon recommends Normal Form as the most flexible, powerful and efficient basis for building a Data Warehouse.