Data Warehouse Architecture

What is Data Warehouse Architecture? Data warehouse architecture refers to the design of an organization’s data collection and storage framework....

What is Data Warehouse Architecture?

Data warehouse architecture refers to the design of an organization’s data collection and storage framework. Because data needs to be sorted, cleaned, and properly organized to be useful, data warehouse architecture focuses on finding the most efficient method of taking information from a raw set and placing it into an easily digestible structure that provides valuable BI insights.

When building an organization’s data warehouse, there are three main types of architecture considered, each with its own benefits and drawbacks.

Data Warehouse Architecture

Single tier warehouse architecture focuses on creating a compact data set and minimizing the amount of data stored. While it is useful for removing redundancies, it isn’t effective for organizations with large data needs and multiple streams.

Two-tier warehouse structures separate the resources physically available from the warehouse itself. While it’s more effective at storing and sorting data, it’s not scalable, and it supports a minimal number of end-users.

Three tier architecture, the most popular type of data warehouse architecture, creates a more structured flow for data from raw sets to actionable insights.

The bottom tier is the database server itself and houses the back-end tools used to clean and transform data. The second tier uses Online Analytical Processing (OLAP) and is the go-between end-users and the warehouse. OLAPS can interact with both relational databases and multidimensional databases, which lets them collect data better based on broader parameters. The top tier is the front-end of an organization’s overall business intelligence suite. This is where users can interact with data via queries, data visualizations, and data analytics tools.

How Can I Use Data Warehouse Architecture?

Establishing which type of database your organization needs and how you plan to interact with it is vital when searching for insights. It is also important to evaluate who is going to be examining data and what sources they need when considering your data warehouse architecture. Although the data warehouse vs data mart debate is not always applicable for smaller organizations, those with more teams, departments, and specific needs may benefit from the latter. Data marts’ specific subject-oriented nature makes them crucial aspects of your overall data warehouse architecture.

Moreover, depending on the size of your organization, different types of warehouse architectures may be more practical. Understanding which is best depends on the currency of your data, the size of your sets, and your organization’s demands.

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