Loosely speaking, a data warehouse can be defined as the decision support database that is maintained separately from the organization’s operational databases. A data warehouse is a type of data management system that is designed to support and enable business intelligence activities, especially analytics, like machine learning.
A data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management’s decision-making process. –Â According to William H.Inmon
Features of data warehouse
Subject oriented
A data warehouse is organized around a major subject such as customer-supplier, product, and sales, rather than concentrating on the day-to-day operating and transactions processing or data warehouse focuses on the modeling and analysis of data for decision-makers.
Hence data warehouse typically provides s simple and concise view of particular subject issues by excluding data that are not useful in the decision support process.
Integrated
A data warehouse is usually constructed by integrating multiple heterogeneous sources, such as relational databases, flat files, and online transaction, records. Data cleaning and data integration techniques are applied to ensure consistency in naming conventions.
Time-Variant
Data are stored to provide information from on historic perspective (eg. the past 5-10 years) every key structure in the data warehouse contains, either implicitly or explicitly, a time element.
Non-volatile
A data warehouse is always a physically separate store of data transformed from the application data found in the operational environment.
Due to this separation, a data warehouse does not require transaction processing, recovery, and concurrency control mechanism. It usually required only two operations in data accessing initial loading of data and access of data.