Introduction to Data Warehouse Schema

Logical descriptions of database are known as Schema. It is the blueprint of the entire database. It defines how the data are organized and how the relations among them are associated. Data warehouse schema consists of the name and description of records including associated data items and aggregates. A database uses relational models whereas a data warehouse uses different types of schema, namely, Star, Snowflake, and Fact Constellation.

To start discussion on these schemas, it is important to understand the basic terminology used in this process, which is discussed below.

Dimension

The term ‘dimension’ in data warehousing is a collection of reference information about a measurable event. These events are stored in a fact table and are known as facts. The dimensions are generally the entities for which an organization wants to preserve records. The descriptive attributes are organized as columns in dimension tables by a data warehouse. For example, a student's dimension attributes could consist of first and last name, roll number, age, gender, or an address dimension that would include street name, state, and country attributes.

A dimension table consists of a primary key column that uniquely identifies each record (row) of dimension. A dimension is a framework that consists of one or more hierarchies that classify data. Usually dimensions are de-normalized tables and may have redundant data.

Let us take a quick recap of the concepts of normalization and de-normalization, as they will be used in this chapter. Normalization is a process of breaking up a larger table into smaller tables free of any possible insertion, updation or deletion anomalies. Normalized tables have reduced redundancy of data. In order to get full information, these tables are usually joined.

In de-normalization, smaller tables are merged to form larger tables to reduce joining operations. De-normalization is particularly performed in those cases where retrieval is a major requirement and insert, update, and delete operations are minimal, as in case of historical data or data warehouse. These de-normalized tables will have redundancy of data.