The design of an organization’s data gathering and storage infrastructure is referred to as data warehouse architecture. Data warehouses are analytical tools designed to help users across several departments make decisions and report on them. For a whole organization, data must be sorted, cleaned, and appropriately organized into a single, unified system. The goal of data warehouse design is to discover the most effective way to extract information from a raw set and organize it into an easily comprehensible data structure that delivers useful business intelligence insights to analysts. It is quite difficult to maintain accuracy and completeness in such a system.
The following are the features of Data Warehouse Concepts:
A data warehouse architecture is subject-oriented as it contains information from all departments. It emphasized data modeling and analysis for decision-making. It also gives a basic and brief picture of the issue by eliminating data that is not useful in supporting the decision-making process.
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In a data warehouse architecture, integration refers to the development of a single unit of measurement for all different types of data. The data should be kept in a form that allows it to be understood by everyone.
The Data warehouse data has date stamps and is used as historical data. Every data set should include a timestamp.
The non-volatile nature of a data warehouse architecture implies that new data has no influence on old data. It aids in the examination of historical data and what occurred when. Data warehouse tasks such as delete and update do not exist.
Data warehouse has three primary forms of architecture :
- Single-tier warehouse design creates a compact data collection while reducing the quantity of data kept. While it is beneficial for eliminating redundancies, it is ineffective for businesses with high data requirements and many streams.
- Two-tier data warehouse architecture separates the physical resources from the warehouse itself. This design is not scalable and cannot handle a high number of end-users.
- A three-tier architecture is a better design since it enables the efficient flow of data from raw to readable data.
Three-tier has 3 types:
- The bottom tier : The database server itself is housed in the bottom layer, as the back-end tools used to clean and modify data.
- The middle tier : The intermediate layer of a data warehouse is an Online Analytical Processing OLAP server, which may be constructed using either the ROLAP or MOLAP models.
- The top tier : Top tier serves as the front end of a company’s comprehensive business intelligence suite.
Overall, the data warehouse architecture is in charge of maintaining systems that keep raw data and other data assets secure and accessible. For students interested in working with data warehouses or the broader subject of business intelligence, there are several fascinating job options accessible. Data architects, database administrators, programmers, and analysts are examples of BI professions. Praxis Business School, a well-known B-School having campuses in Kolkata and Bangalore, provides 9-month industry-driven Post Graduate Programs in Data Science. Our PGP course in Data Science aims to offer students with the skills, methodologies, and talents required for a smooth transition into the field of Analytics and advancement into Data Scientist positions.
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