Different types of data warehouses

In addition to the relational database, an enterprise data warehouse environment often consists of an Extract Transform and Load (ETL) solution, an OLAP engine (hooray Essbase), client analysis tools, and other web or desktop applications that manage the gathering of data and delivering it to business users.

There are three types of data warehouses:

In case you're wondering, here are a few words about an OLAP solution and an OLTP solution. An OLAP solution stands for On-Line Analytical Processing, which in a nutshell means that the data you are using for your analysis is mainly considered reporting or presentation data and any updates or write-backs are solely for analytical purposes. The source data is rarely updated in this method.

The OLTP solution stands for On-Line Transactional Processing which means that the base or source data is directly updated with factual and historical data as an output of the analysis or data entry processes. Conventional straight line reporting can be performed and there is very little, if any, slice-and-dice analysis or what-if scenarios.

Data warehouses and data marts are usually built on dimensional data modeling where fact tables are connected with dimension tables. This is most useful for users to access data since a database can be visualized as a cube containing many dimensions. A data warehouse and its smaller, more specific data mart provide an opportunity for slicing and dicing that visualize cube along any one of its dimensions.