The term “Data Warehouse” was first coined by Bill Inmon in 1990. According to Inmon, a data Warehouse is a subject oriented, integrated, time-variant, and non-volatile collection of data. This data helps analysts to take informed decisions in an organization.

A data warehouses provides us generalized and consolidated data in multidimensional view Along with generalized and consolidated view of data, a data warehouses also provides us Online Analytical Processing (OLAP) tools. These tools help us in interactive and effective Analysis of data in a multidimensional space. This analysis results in data generalization and Data mining.

  • A data warehouse is a database, which is kept separate from the organization operational database.
  • There is no frequent updating done in a data warehouse.
  • It possesses consolidated historical data, which helps the organization to analyze its business.
  • A data warehouse helps executives to organize, understand, and use their data to take strategic decisions.
  • Data warehouse systems help in the integration of diversity of application systems.
  • A data warehouse system helps in consolidated historical data analysis.
Why a Data Warehouse is separated from Operational Databases

A data warehouses is kept separate from operational databases due to the following reasons:

  • An operational database is constructed for wel1-known tasks and workloads such as searching particular records, indexing, etc. In contract, data warehouse queries are often complex and they present a general form of data.
  • Operational databases support concurrent processing of multiple transactions. Concurrency control and recovery mechanisms are required for operational databases to ensure robustness and consistency of the database.
  • An operational database query allows reading and modifying operations, while an OLAP query needs only read only access of stored data.
  • An operational database maintains current data. On the other hand, a data warehouse maintains historical data.
Features of a data warehouse
  • 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 product, customers, suppliers, sales, revenue, etc. A data warehouse does not focus on the ongoing operations; rather it focuses on modelling and analysis of data for decision-making.
  • Integrated – A data warehouse is constructed by integrating data from heterogeneous sources such as relational databases, flat files, etc. This integration enhances the effective analysis of data.
  • Time Variant- the data collected in a data warehouse is identified with a particular time. The data in a data warehouse provides information from the historical point of view.
  • 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 operational database is not reflected in the data warehouse.
Types of Data Warehouse

Information processing, analytical processing and data mining are the three types of data warehouse applications that are discussed below

  • Information Processing- A data warehouse allows to process the data stored in it. The data can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.
  • Analytical Processing- A data warehouse supports analytical processing of the information stored in it. The data can be analyzed by means of basic OLAP operations, including slice-and-dice, drill down, drill up, and pivoting.
  • Data Mining – Data mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction. These mining results can be presented using the visualization tools.

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