Data warehouses provide companies with extensive benefits because they enable them to analyze large volumes of diverse data, extract significant value from it, and store historical records.

These unique benefits are available because of four distinctive features of data warehouses, as described by computer scientist William Inmon. According to his definition, data warehouses have the following characteristics.

• Subject-oriented. Data warehouses can be used to analyze data that relate to a single topic or functional area (e.g., sales).
• Uniformity. Data warehouses ensure the integrity of different types of data from different sources.
• Immutability. Data elements placed in a data warehouse are not subject to change.
• Changes over time. Analysis of data placed in a data warehouse is designed to identify changes in patterns that occur over time.
• A well-designed data warehouse provides fast queries, efficient flow of large volumes of data, and enough flexibility so that end users can form longitudinal and cross-sectional slices of the data or reduce its size for more granular examination, thus meeting a wide variety of data examination needs at both the top and the bottom level. Data warehouses provide the functional foundation for middleware business intelligence environments that give end users access to reports, dashboards, and other interface elements.

Data warehouse architecture
The architecture of the data warehouse depends on the needs of the company. The most common types of architectures are as follows.

• Simple. All data warehouses share a common design, where metadata, summary data, and raw data are stored in a central repository of the warehouse. The repository receives data from sources and is then accessed by end users to perform analysis, reporting, and exploration.
• Simple architecture with a preparation area. Operational data must be cleansed and processed before being placed in the repository. This can be done programmatically, but many data warehouses have a special area where the data is processed before it goes directly into the repository.
• Primary and auxiliary stores. Adding data silos between the central repository and end users enables companies to use data warehouses to serve different lines of business. When the data is ready to be used, it is placed in the appropriate storefront.
• “Sandboxes. “Sandboxes are secure private and private areas where companies can quickly explore new data sets or ways to analyze without having to ensure compliance with formal data warehouse rules and protocols.