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Data integration describes processes, tools and concepts to combine heterogeneous data sources into consistent, usable views. It covers extraction, transformation, harmonization and consolidation for analytics, operations and decision support. Goals include semantic coherence, improved data quality and reliable access points across architectures and governan…
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Data integration brings heterogeneous sources into a shared, reliable data view so analytics and operational processes can rely on consistent information.
Data integration arose from the practical problem of making information from separate databases, files, and later web and operational sources usable together. Since the early 1980s, researchers developed interoperability and shared access layers; in 1991, IPUMS showed how ETL and a unified view schema could combine large heterogeneous collections. The W3C now gathers best practices for web data that should be discoverable, understandable, and reusable.
Think of data integration as a layer between sources and consumers. At the bottom, business systems, sensors, or APIs deliver data in their own formats and meanings. The integration layer maps fields, transforms values, checks quality, and resolves semantic differences. At the top, this produces either a physically consolidated store, such as a warehouse or hub, or a virtual view that connects multiple sources at query time.
The connected systems differ in structure, format, freshness, and business meaning.
Fields, codes, and entities are systematically mapped between source schemas and the target view.
Different formats, units, and terms are brought to a shared meaning.
Data is physically combined into a common store and managed there.
The same term can mean different things in different sources and must be resolved.
Virtual integration links sources at query time; physical integration loads data ahead of time into a target store.
Data integration matters when business units need the same metrics from multiple systems, when siloed data must be combined, or when operational processes depend on master and reference data. It is useful in ERP, platform, and sensor environments, but it requires ongoing work for mappings, data quality, and governance. Physical integration often improves stability and query performance; virtual integration avoids extra copies, but can depend on live sources and add runtime complexity.
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