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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).
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".
Some common use cases for Amazon Kinesis include: [2] Real-time analytics: Analyzing streaming data in real time to provide immediate insights and make data-driven decisions. Log and event data collection: Collecting, processing, and analyzing log and event data generated by applications, infrastructure, and devices. [12]
An integration competency center (ICC), sometimes referred to as an integration center of excellence (COE), is a shared service function providing methodical data integration, system integration, or enterprise application integration within organizations, particularly large corporations and public sector institutions.
An integration operation could be short-lived (e.g., keeping data in sync across two applications could be completed within a second) or long-lived (e.g., one of the steps could involve the EAI system interacting with a human work flow application for approval of a loan that takes hours or days to complete). [citation needed]
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 ]
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