Explain Data Warehouse Features /Characteristics / Nature of Data Warehouse.

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 Data Warehouse Features /Characteristics / Nature of Data Warehouse

The key features of a data warehouse are:

  • Subject-oriented: A data warehouse is organized around major subjects such as customer, supplier, product, and sales. Rather than concentrating on the day-to-day operations and transaction processing of an organization, a data warehouse focuses on the modeling and analysis of data for decision-makers. Hence, data warehouses typically provide a 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, encoding structures, attribute measures, and so on.
  • Time-variant: Data is stored to provide information from a historic perspective (e.g., the past 5-10 years). Every key structure in the data warehouse contains, either implicitly or explicitly, a time element.
  • Nonvolatile: 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 mechanisms. It usually requires only two operations in data accessing: initial loading of data and access of data.

                                             OR,

  • Subject Oriented: A data warehouse is subject-oriented because it provides information around a subject rather than the organization's ongoing operations. These subjects can be the product, customers, suppliers, sales, revenue, etc.
  • Integrated: A data warehouse is constructed by integrating data from heterogeneous sources such as relational databases, flat files, etc. Data cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attribute measures, and so on.
  •  Time-Variant: The data collected in a data warehouse is identified with a particular time period. The data in a data warehouse provides information from the historical perspective (e.g., the past 5–10 years).
  • Non-volatile: Non-volatile means the previous data is not erased when new data is added to it. A data warehouse is kept separate from the operational database and therefore frequent changes in the operational database are not reflected in the data warehouse.

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