The market for data integration tools consists of stand-alone software products that enable organizations to combine data from multiple sources and perform tasks related to data access, transformation, enrichment and delivery. They enable use cases such as data engineering, delivering modern data architectures, self-service data integration, operational data integration and supporting AI projects. Data management leaders procure data integration tools for their teams, including data engineers and data architects, or for other users, such as business analysts or data scientists. These products are primarily consumed as SaaS or deployed on-premises, in public or private cloud, or in hybrid configurations.
Data virtualization technology is based on the execution of distributed data management processing, primarily for queries, against multiple heterogeneous data sources, and federation of query results into virtual views. This is followed by the consumption of these virtual views by applications, query/reporting tools, message-oriented middleware or other data management infrastructure components. Data virtualization can be used to create virtualized and integrated views of data in-memory, rather than executing data movement and physically storing integrated views in a target data structure. It provides a layer of abstraction above the physical implementation of data, to simplify querying logic.