Type of Data Warehouse Architectures
In today’s data-driven world, data warehousing has become prominent among various agencies for storing and analyzing large amounts of data. Thus, choosing the right architecture is crucial if you want to build an efficient data management system. In this blog, we have discussed different types of data warehouse architecture, and each type has its unique features. So, without any further delay, let’s get started.
An Overview of Data Warehouse Architecture
Data Warehouse Architecture defines the design and structure of a system used to manage, organize, and implement data within an organization. It combines data from various sources and centralizes it under a single architecture.
If your data is well-designed, it will be collected from multiple sources, cleansed, stored, and easily accessible. Data warehouse architecture makes data analysis perfect, by making data handling and data storage convenient. In addition, the architecture of a data warehouse uses a structured framework for storing and managing data.
What are the Advantages of Using Data Warehouse Architecture?
There are multiple advantages associated with using the three-tier data warehouse architecture, let’s go through some of them:
- By using data warehouse architecture, users can handle large amounts of data without affecting the performance.
- It can also lead to enhanced business intelligence by creating reports, dashboards, and visualization tools.
- Data Warehouse architecture offers centralized data storage, which helps in easily accessing and managing data from various departments.
Different Types of Data Warehouse Architecture
There are three different kinds of data warehouse architecture, used to build a well-structured and designed data warehouse. Let’s have a look at all of them, one by one:
Single Tier Architecture
The single layer architecture of a data warehouse stores data in one layer and reduces data storage by creating a more streamlined data set. It enhances the quality of data by removing redundancy. Additionally, it directly connects data warehouses to analytical interfaces.
Two Tier Architecture
Two Tier Architecture lies between the source layer and the data warehouse layer. It comprises a data staging area that makes your data clean before loading it into the data warehouse. Once the data is stored, it can be examined for data analysis. In addition, it offers enhanced scalability and better performance.
Three Tier Architecture
Three Tier Architecture is a structured framework, divided into three distinct layers, where each tier has its specific role. Let’s go through all the layers in detail:
- Bottom Tier (Data Storage Layer): The bottom tier acts as an essential framework in a three-tier architecture. It stores raw data from various sources before the data is processed. Also, it uses ETL, which stands for Extract, Transform, and Load. It is a kind of tool that cleans as well as integrates the data.
- Middle Tier (Data Processing and Transformation Layer): The middle tier transforms raw data into structured data. This tier acts as a link between data storage and visualization tools. Additionally, it uses an Online Analytical Processing server that retrieves data to enhance scalability, which leads to efficient data analysis.
- Top Tier (Data Presentation Layer): In top-tier data warehouse architecture, the user interacts with the data warehouse using various analytics, reporting, query, dashboard, and data mining tools. Furthermore, it also uses Business Intelligence (BI) tools to create reports, dashboards, and visualizations to facilitate better user interaction.
To Conclude
When choosing data warehouse architecture, it is important to choose the one that fits your long-term business goals. If you are a small or medium-sized business, then go with the single-tier or two-tier architecture. Whereas, if you have a large organization, you can opt for three-tier architecture. Moreover, you should also be aware of the benefits of using different types of data warehouse architecture.
