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Data warehouse models are used to build physical applications of computer system data models. There are several types, including Flat, Hierarchical, Relational, and Dimensional. Relational and Dimensional models are the most common, with other types being minor improvements to the basic methods of organizing.
A data warehouse model is an applied form of a computer system data model. In computer systems, data flow is modeled based on theoretical information in order to test the capabilities and limitations of the system. When data warehousing was born, these same models began to find real physical applications in building data. It would be similar to a person just doing math problems, and then using those equations to build a new kind of engine.
There are several ideas behind a data warehouse model. They each have their own strengths and weaknesses, as well as types of data they are best suited to handle. It is not uncommon for several data warehouse models or hybrid systems that leverage the strengths of multiple types to exist in the same system.
The Flat system is the type of data warehouse model that many users would recognize as the simplest. This way of storing data has interconnected rows and columns of information, similar to a spreadsheet. While this method is easy for humans to read, it is harder for computers and slow to correlate.
A hierarchical data warehouse model stores information in a continuous series of levels. Each level contains information that depends on the previous generation. This is very similar to the network model, which also contains a number of dependent layers. The difference is in their dependencies: in a hierarchical system, each block of data can only have one higher level of dependency, but in a network model they can have as many as they need. In both cases, a single block of information can connect to multiple underlying blocks.
A relational data warehouse model is a variant of the Flat system. The data is contained in tables, similar to those of a flat system, and each master data is assigned a unique identifier. This identifier travels together with the data, ensuring its uniqueness. For example, if an employer has two employees with the same name, their identifier would still separate them in the system. This identifier is correlated across the entire system: hopefully, every time something related to unique information enters the system, it correlates with pre-existing information based on the identifier alone.
The dimensional model is based on the Hierarchical. A single fact is used as a starting point, then further information related to that fact ensues. Something like an employee number would be an initial fact; then dates related to that worker, as well as pay or vacation, would link to it when they enter the system.
Relational and dimensional data warehouse models are the two most common styles. There are other types of data warehousing, but they are minor compared to these. Typically, new models are built on Flat, Hierarchical or Network systems; often, they’re just simple improvements to their basic method of organizing.
