Star schema Snowflake schema Constellation schema

DataWarehouse Architecture

The main difference between the database architecture in a standard, on-line transaction processing oriented system (usually ERP or CRM system) and a DataWarehouse is that the system’s relational model is usually de-normalized into dimension and fact tables which are typical to a data warehouse database design.
The differences in the database architectures are caused by different purposes of their existence.
In a typical OLTP system the database performance is crucial, as end-user interface responsiveness is one of the most important factors determining usefulness of the application. That kind of a database needs to handle inserting thousands of new records every hour. To achieve this usually the database is optimized for speed of Inserts, Updates and Deletes and for holding as few records as possible. So from a technical point of view most of the SQL queries issued will be INSERT, UPDATE and DELETE.

Opposite to OLTP systems, a DataWarehouse is a system that should give response to almost any question regarding company performance measure. Usually the information delivered from a data warehouse is used by people who are in charge of making decisions. So the information should be accessible quickly and easily but it doesn't need to be the most recent possible and in the lowest detail level.
Usually the data warehouses are refreshed on a daily basis (very often the ETL processes run overnight) or once a month (data is available for the end users around 5th working day of a new month). Very often the two approaches are combined.

The main challenge of a DataWarehouse architecture is to enable business to access historical, summarized data with a read-only access of the end-users. Again, from a technical standpoint the most SQL queries would start with a SELECT statement.

In Data Warehouse environments, the relational model can be transformed into the following architectures:

  • Star schema
  • Snowflake schema
  • Constellation schema