Metadata-Driven Data Integration Framework: Automating Enterprise Data Integration Through Declarative Approaches

Abstract

Metadata-focused data integration frameworks are revolutionising how companies handle data. The basic ideas of data integration, their application procedures, and their usage patterns in practical applications are all covered in this article. Because the logic is stored in metadata repositories, these techniques facilitate the integration process. By separating what they need from how it's done, businesses may establish data structures and mappings without doing a lot of manual coding. The design prioritises ease of expansion and modularity. Runtime environments that control data transfer based on the metadata, execution engines that generate integration workflows on the fly, and metadata repositories that serve as knowledge bases make up its three primary components. Implementation strategies look to resolve technological issues like building management tools, modelling metadata, and enhancing performance through caching and parallel processing. Shorter development times, simpler maintenance, improved data quality due to centralised validation, increased organisational flexibility, and obvious scalability are the outcomes. However, there are still problems with how organisational changes are handled. There are other problems with handling the complexity of metadata and improving real-time performance. AI has a lot to offer in the fields of automatic mapping, creating platform-neutral cloud-native systems, and creating user-friendly interfaces for commercial clients