Dimensional Modeling - Design Changes

The following changes are anticipated to the design after the data warehouse is up and running

1. Adding new unanticipated facts (that is, new additive numeric fields in the fact table), as long as they are consistent with the fundamental grain of the existing fact table.

2. Adding completely new dimensions, as long as there is a single value of that dimension defined for each existing fact record.

3. Adding new, unanticipated dimensional attributes.

4. Breaking existing dimension records down to a lower level of granularity from a certain point in time forward.

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Dimensional Vs ER Modeling

1. The relationship in a Dimensional model don't represent business rules instead they are navigational paths used to help write reports or create graphs. But whereas relationship in ER modeling represents business rules.
2. The primary goal of the ER modeling is to remove all non key data redundancy.
But Dimensional modeling controls data redundancy by confirming dimension and fact tables.
The table that has been confirmed can be used in more than one dimensional data model.

How to Create a Dimensional Model

1. Identity business process by business process and Each business process can be expressed
as a data mart
---- a modular , highly focused, richly detailed, incrementally designed componenet
of the datawarehouse.
Initially try to focus on the Single-Source data mart not on multiple source datamat.
example of single data marts are retail sales, purchase orders, shipments and payments.
example of multiple data mart is Customer profitability which combines revenue and costs that often come from sales and inventory databases.

2. Grain of the Fact table which is the level of detail that the table captures.