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,
This helps in:
- Maintaining historical records
- Analyzing the data to gain a better understanding of the business and to improve the business.
In addition to a relational database, a data warehouse environment can include an extraction, transportation, transformation, and loading (ETL) solution, statistical analysis, reporting, data mining capabilities, client analysis tools, and other applications that manage the process of gathering data, transforming it into useful, actionable information, and delivering it to business users.
Data warehousing should be following characteristics
Data warehouses are designed to help you analyse data. For example, to learn more about your company’s sales data, you can build a data warehouse that concentrates on sales. Using this data warehouse, you can answer questions such as “Who was our best customer for this item last year?” or “Who is likely to be our best customer next year?” This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.
Non-volatile means that, once entered into the data warehouse, data should not change. This is logical because the purpose of a data warehouse is to enable you to analyse what has occurred.
A data warehouse’s focus on change over time is what is meant by the term time variant. In order to discover trends and identify hidden patterns and relationships in business, analysts need large amounts of data. This is very much in contrast to online transaction processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive.