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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.

What is the concept of data warehousing?

Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making.

What is data warehouse modeling?

A data model is a graphical view of data created for analysis and design purposes. Data modeling includes designing data warehouse databases in detail, it follows principles and patterns established in Architecture for Data Warehousing and Business Intelligence.

What are the different types of data warehouse architecture?

First of all, it is important to note what data warehouse architecture is changing. Companies are increasingly moving towards cloud-based data warehouses instead of traditional on-premise systems. The new cloud-based data warehouses do not adhere to the traditional architecture; each data warehouse offering has a unique architecture. See the full article for more details.

What is star schema in SQL?

In computing, the star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema consists of one or more fact tables referencing any number of dimension tables.

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.

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