Modern Data Warehouse Architecture Types

A data warehouse is a centralized repository that stores structured, organized data from various sources for the purpose of analysis, reporting, and decision-making.

Focusing on the subject rather than on operations, the DWH integrates data from multiple sources, giving the user a single source of information in a consistent format. Since it is non-volatile, it records all data changes as new entries without erasing its previous state. This feature is closely related to being time-variant, as it keeps a record of historical data, allowing you to examine changes over time.

All of these properties help businesses create analytical reports needed to study changes and trends.

Data Warehouse Architecture:

There are three ways you can construct a data warehouse system. These approaches are classified by the number of tiers in the architecture. Therefore, you can have:

• Single-tier architecture

• Two-tier architecture

• Three-tier architecture

 Single-Tier Architecture:

Description: Single-tier architecture, also known as monolithic architecture, refers to a setup where all components of the data warehouse system are installed and run on a single machine or server. In this architecture, the data storage, processing, and presentation layers are combined into a single system. The main goal of having such an architecture is to remove redundancy by minimizing the amount of data stored. 

Two-tier Data Warehouse Architecture

Description: A two-tier architecture includes a staging area for all data sources, before the data warehouse layer. By adding a staging area between the sources and the storage repository, you ensure all data loaded into the warehouse is cleansed and in the appropriate format.

Three-tier Data Warehouse Architecture

The three-tier approach is the most widely used architecture for data warehouse systems.

1. The bottom tier is the database of the warehouse, where the cleansed and transformed data is loaded.

2. The middle tier is the application layer giving an abstract view of the database. It arranges the data to make it more suitable for analysis. This is done with an OLAP server.

3. The top-tier is where the user accesses and interacts with the data. It represents the front-end client layer. You can use reporting tools, query, analysis or data mining tools.

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