Design Patterns

Description

{Add more here about (CIF)}

A data mart is a subject-oriented database object streamlined to facilitate reporting and analytics. They contain the essential information that aligns with the business unit and business domains. A data mart is typically a flattened structure and carries forward the necessary attribution to minimize complex joins. To improve the end-user experience, data marts will include the required joins and multi-source attributions into one object.

Description

Dimensional modeling (DM) is part of the Business Dimensional Lifecycle methodology developed by Ralph Kimball which includes a set of methods, techniques and concepts for use in data warehouse design. The approach focuses on identifying the key business processes within a business and modelling and implementing these first before adding additional business processes, a bottom-up approach.

The business process model consists of different object types that link together using primary key that is meaningless and has not business content.

Design Methodology

Dimension Modeling - There are two different approaches to the goal of a dimension Data Warehouse dimension model.

Fact Table

Description

In simple terms, Facts store Events, Countable attributes and Dates. They are linked to dimensions to get the who, what, and where of a fact. The Natural Key (NK) of a fact is derived from the combination of the dimension keys. The NK of a fact is also referred to as the Grain of a Fact. Data changes are observed for a given NK. Most all fact tables link to a Date dimension to record the moment in time the event occurred.

There are different kind of Fact Tables. Each with a specific purpose and set of control attributes.

Dimension Type 2

Description

Flattened structure that stores attribute information for one class of data (e.g. person, place, entity). In simple terms, dimensions give who, what, where of a fact. One or more records having the same natural key can exist. 

Dimension Type 1

Description

Flattened structure that stores attribute information for one class of data (e.g. person, place, entity). In simple terms, dimensions give who, what, where of a fact. Limited A2B Data control attributes are added to record last update information.

General examples of Type 1 dimensions:  Shift, Department, US States, Zipcodes