A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
A Data Warehouse is simply a consolidation of data from a variety of sources that is designed to support strategic and tactical decision making. Its main purpose is to provide a coherent picture of the business at a point in time.
Using various Data Warehousing toolsets, users are able to run online queries and 'mine" their data.
A Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated. This makes it much easier and more efficient to run queries over data that originally came from different sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading solution, an online analytical processing engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.
Types of Datawarehouse
Enterprise Data Warehouse: Provides a central database for decision support throughout the enterprise.
ODS(Operational Data Store) : Having a broad enterprise wide scope, but unlike real enterprise data warehouse, data is refreshed in real time and used for routine business activities
Data Mart: A subset of data warehouse, it is design for particular lines of business such as sells, marketing or finance, or in any organization documents of a particular department will be data mart
Characteristics of a data warehouse
Subject Oriented
Integrated
Nonvolatile
Time Variant
Subject Oriented
Data warehouses are designed to help you analyze data. For example, to learn more about your company's sales data, you can build a warehouse that concentrates on sales. Using this warehouse, you can answer questions like "Who was our best customer for this item last year?"
This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.
Integrated
Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated.
Nonvolatile
Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.
Time Variant
In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing systems, where performance requirements demand that historical data be moved to an archive. A data warehouse's focus on change over time is what is meant by the term time variant.
Data Warehouse Configurations
A Data Warehouse configuration, also known as the logical architecture, includes the following components:
One Enterprise Data Store - a central repository which supplies atomic integrated information to the whole organization.
One Operational Data Store - a "snapshot" of a moment in time's enterprise-wide data.
One or more individual Data Mart - summarized subset of the enterprise's data specific to a functional area or department, geographical region, or time period
One or more Metadata Store - or Repository catalog of reference information about the primary data. Metadata is divided into two categories: information for technical use, and information for business end-users.
