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Data Warehouse and Data mart overview, with Data Marts shown in the top right. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence. [1] Data warehouses are central repositories of data integrated from ...
The Kimball lifecycle is a methodology for developing data warehouses, and has been developed by Ralph Kimball and a variety of colleagues. The methodology "covers a sequence of high level tasks for the effective design, development and deployment" of a data warehouse or business intelligence system. [1]
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
Collecting data from numerous sources and analyzing it using different data analysis tools has its advantages, including overcoming the risk of method bias; using data from different sources and analyzing it using multiple analysis methods guarantees businesses and organizations robust and reliable findings they can use in decision making.
The user, rather than the database itself, typically initiates data curation and maintains metadata. [8] According to the University of Illinois' Graduate School of Library and Information Science, "Data curation is the active and on-going management of data through its lifecycle of interest and usefulness to scholarship, science, and education; curation activities enable data discovery and ...
A data mart is a structure/access pattern specific to data warehouse environments. The data mart is a subset of the data warehouse that focuses on a specific business line, department, subject area, or team. [1] Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department.
Data mesh is based on four core principles: [17] Domain ownership; Data as a product [18]; Self-serve data platform; Federated computational governance; In addition to these principles, Dehghani writes that the data products created by each domain team should be discoverable, addressable, trustworthy, possess self-describing semantics and syntax, be interoperable, secure, and governed by ...
The purpose of the data collection and any (known) data mining projects. How the data will be used. Who will be able to mine the data and use the data and their derivatives. The status of security surrounding access to the data. How collected data can be updated. Data may also be modified so as to become anonymous, so that individuals may not ...