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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).
Business Objects's Data Integrator is a data integration and ETL tool that was previously known as ActaWorks. Newer versions of the software include data quality features and are named SAP BODS (BusinessObjects Data Services). The Data Integrator product consists primarily of a Data Integrator Job Server and the Data Integrator Designer.
Core data integration is the use of data integration technology for a significant, centrally planned and managed IT initiative within a company. Examples of core data integration initiatives could include: ETL (Extract, transform, load) implementations; EAI (Enterprise Application Integration) implementations
Web data integration (WDI) is the process of aggregating and managing data from different websites into a single, homogeneous workflow. This process includes data access, transformation, mapping, quality assurance and fusion of data. Data that is sourced and structured from websites is referred to as "web data".
In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration [1] and data management tasks such as data wrangling, data warehousing, data integration and application integration.
That more than doubled the station's previous 12-hour snowfall record in 26 years of data, according to the JMA. Five other locations in Hokkaido also set new 12-hour snowfall records since the ...
A data architecture aims to set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration , for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems.
Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. [1] It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). [ 2 ]