Master Data Management (MDM) ensures data integrity, consistency, and reliability across an organization's systems. I introduce a novel complex match and merge algorithm optimized for real-time MDM solutions. The proposed method accurately identifies duplicates and consolidates records in large-scale datasets by combining deterministic matching, fuzzy matching, and machine learning-based conflict resolution. I implemented it using PySpark and Databricks; the algorithm benefits from distributed computing and Delta Lake for scalable and reliable data processing. Comprehensive performance evaluations demonstrate a 90% accuracy on datasets of up to 10 million records while maintaining low latency and high throughput, significantly improving upon existing MDM approaches. The method shows strong potential in domains such as healthcare and finance, with an overall 30% improvement in latency compared to traditional MDM systems.

翻译：主数据管理（MDM）可确保组织各系统间的数据完整性、一致性与可靠性。本文提出一种专为实时MDM解决方案优化的新型复杂匹配与合并算法。该方法通过结合确定性匹配、模糊匹配及基于机器学习的冲突消解机制，能够在大规模数据集中精确识别重复记录并实现记录整合。研究采用PySpark与Databricks平台实现该算法，其分布式计算能力与Delta Lake架构为可扩展、可靠的数据处理提供了支撑。综合性能评估表明，该算法在千万级记录数据集上达到90%的准确率，同时保持低延迟与高吞吐量，较现有MDM方法有显著提升。本方法在医疗与金融等领域展现出巨大应用潜力，与传统MDM系统相比整体延迟降低了30%。