Autonomous Metadata Correction Engines for Stream Data: A Rule-Based AI Approach for Schema Drift Recovery in Financial Pipelines

In the realm of real-time financial data pipelines, this dissertation tackles the crucial issue of upholding data integrity and consistency when schema drift occurs, achieved through the creation of an autonomous metadata correction engine. This engine is guided by a rule-based AI approach. The research methodically gathers and examines different stream data samples, showcasing a range of schema variations. This comprehensive approach enables thorough training of the algorithm, equipping it to identify and correct metadata discrepancies promptly. Findings suggest a marked enhancement in both accuracy and efficiency in schema drift management using the engine, proving its adaptive capacity to handle evolving data structures, generally speaking, with little human oversight. [citeX] the importance of these outcomes isn't limited to finance alone; healthcare also stands to benefit significantly, particularly where real-time data accuracy is key for informed decisions and improved patient safety. By guaranteeing dependable data streams, this research presents opportunities for advancements in healthcare analytics and improvements to operational workflows, thus resulting in better patient outcomes. [extractedKnowledgeX] More generally, this study implies that automated systems managing schema drift could revolutionize data management practices in numerous industries, essentially ushering in a new era where AI-driven solutions are trusted to maintain data quality in dynamic settings.