ETL, Business Intelligence & Data Modelling, design Data Marts, and Data Warehouses. The database is the heart of the information processing organization. The organization wants to record transactions; this can be done with a transaction-processing database.
When it comes to collecting and storing data, both data warehouse and data mart are used for the same reason. Each one differs in terms of the amount of data or information it stores. As the name implies, a data warehouse is an information-oriented database, while a data mart is a logical subset of a full data warehouse.
Simply said, a data mart is a data warehouse with a limited scope whose data can be retrieved by summarising and selecting the data from the data warehouse or by employing independent data extraction, transformation, and loading processes from the source data systems.
The organization also wants to know facts about important business processes such as speed of action, trends, statistics about processing, and other matters in order to be able to execute decision-making on a tactical and strategic level. In short, people want decision support information that can be provided from Business Intelligence.
Data Warehouse
According to Wikipedia, the word data warehouse refers to an integrated group of data that can be accessed at any point in time and is non-volatile. It is used to aid in the decision-making process of management. An alternative definition is that of an information warehouse, which is an online repository of information acquired from numerous sources and stored in an integrated schema. Data is collected and stored for a long time, so it has access to historical knowledge and lasts for a long period.
Due to its single integrated interface to data, it enables users to develop decision support queries quickly and easily. To transform data into information, a data warehouse is needed. A top-down strategy is required when designing a data warehouse.
You can find out about customers, sales, assets, and things. Fact constellation scheme is employed throughout, which covers a wide range of subjects. One of the main characteristics of a data warehouse is its dynamic nature.
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Conclusion
Enterprise view is provided by data warehouse while data mart is a subset of data warehouse that offers a departmental view and decentralized storage. A large and integrated data warehouse carries a high failure risk and is difficult to establish. Data marts are also easy to develop and have a lower failure risk, but they can become fragmented as a result of their ease of construction and related low failure risk.
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