Three Tier Data Warehouse Architecture – In this article we will introduce you to the most common data warehouse architecture.

Nowadays, business processes are increasingly supported by digital assistance systems and recorded for further analysis and optimization.
This generates a lot of structured, unstructured and semi-structured data from many different sources.

What is a Data Warehouse System?

In order to create a unified view of the data for improved BI and thus enable comprehensive evaluations, all information from the diverse data sets must be centralized.

This integration is the first basic function of a data warehouse system. However, this information system also assumes the task of data separation. In this way, data that is used for operational business, i.e. that is regularly queried, can be separated from data that is only used for analyzing business processes in controlling.
You can read more about the different types of data warehouses here.

Data centralization ensures that there is only one version of the truth for a company to use for decision making and forecasting.

What does the Typical Data Warehousing Architecture look like?

The complexity of this system increases exponentially with the complexity of the business. Many distinctive data sources, i.e. business processes, provide commutative and historical data. Therefore, basic approaches have been defined according to which every data warehouse system should be structured. Single Tier, Two Tier and Three Tier.

2 Tier vs Three Tier Data Warehouse Architecture

In the following we will work out the three tier architecture.
This, the most commonly used, structure is completely decoupled from the data and the user interface by moving the application logic to a middle tier.
In two-tier, the application logic resides either in the user interface on the client or in the database on the server.
Thus, without a middle tier, this system is less scalable and more flexible. Integration of other data sources is more difficult here.

Three Tier Data Warehouse Architecture

The Three Tier Data Warehouse Architecture is the design on the basis of which a data warehouse with three tiers is then built. The figure below shows this structure with common components.

In this schema, the typical three tier data warehouse architecture is presented in a clear and simplified way
Three Tier Data Warehouse Architecture

However, the individual components can vary and depend on the project framework. As a rule, however, these changes do not alter the basic structure.

Bottom Tier

The lowest layer is persistence, which is usually located on a server. The data from various data sources is prepared and stored here using an ETL (extract, transform and load) process. Tools and other external resources can be used to feed the data.
This persistence can consist of a relational but also a multidimensional database system.

Middle tier

One or more OLAP (Online Analytical Processing) servers reside in the middle data warehouse layer. This technology can be used to create complex budget plans and perform analyses cost-effectively. So in the three tier data warehouse architecture, jobs are generated in the top tier and sent to this middle tier. Here, the data in the bottom tier is then accessed and analyses are performed. The result is then sent to the top tier and thus made available to the user, and/or forwarded to the bottom tier for storage of the analysis results in persistence.

What is an OLAP Server?

Basically, three OLAP server models are distinguished.
In Relational OLAP (ROLAP) the operations on multidimensional data are based on standard relational operations. The Multidimensional OLAP (MOLAP) directly implements the multidimensional data operations. A mixture of relational and multidimensional processing can be handled by Hybrid OLAP (HOLAP).
The choice of the server model always depends on the data composition in the lowest layer.


The top tier is the top of the three tier data warehouse architecture, the front-end client layer. It contains query and reporting tools, analysis tools, and data mining tools, thus providing the interface to the user. Here he can generate analyses and take a look at the data.