Data Warehousing Concepts

Several concepts are of particular importance to data warehousing. They are discussed in detail in this section.

Dimensional Data Model: Dimensional data model is commonly used in data warehousing systems. Dimensional modeling is a design technique that puts the data in a standard framework and provides easy access. A dimension model consists of one fact table and multiple dimension tables which are smaller. (Kimball). Two kinds of schemas are used when designing data models, either a star schema or a snowflake schema. (Dimensional Data Model)

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Slowly Changing Dimension: This is a common issue among data warehousing practitioners. This applies to cases where the attribute for a record varies over time. We give an example below:

Christina is a customer with ABC Inc. She first lived in Chicago, Illinois. So, the original entry in the customer lookup table has the following record:

At a later date, she moved to Los Angeles, California on January, 2003. How should ABC Inc. now modify its customer table to reflect this change? This is the “Slowly Changing Dimension” problem.

There are in general three ways to solve this type of problem, and they are categorized as follows:

Type 1: The new record replaces the original record. No trace of the old record exists.

Type 2: A new record is added into the customer dimension table. Therefore, the customer is treated essentially as two people.

Type 3: The original record is modified to reflect the change.

We next take a look at each of the scenarios and how the data model and the data looks like for each of them. Finally, we compare and contrast among the three alternatives.

Conceptual, Logical, and Physical Data Model: Different levels of abstraction for a data model. This section explains their differences and lists the steps for constructing each.

MOLAP, ROLAP, and HOLAP: What are these different types of OLAP technology? Discuss MOLAP ,ROLAP and HOLAP how they are different from the other, and the advantages and disadvantages of each.

Bill Inmon vs. Ralph Kimball: These two data warehousing heavyweights have a different view of the role between data warehouse and data mart.