Abstract
Data integration combines data from different sources and brings it together to ultimately provide a unified view. If an enterprise has inconsistent data, it is highly likely that it has a data integration problem. The data integration architecture represents the workflow of data from multiple systems of record through a series of transformations used to create consistent, conformed, comprehensive, clean, and current information for business analysis and decision making. This architecture requires a broad set of design, development, and deployment standards. Designing the data integration processes involves creating stage-related conceptual and logical data integration process models and designing stage-related physical data integration process models, stage-related source to target mappings and the overall data integration workflow. Design specifications include conceptual, logical, and physical data integration process models; logical and physical data models for sources and targets; and source to target mappings. A data integration effort needs to accommodate the need to load historical data, and it should include prototyping and testing.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
