AWS Entity Resolution launches support for incremental Machine Learning based matching workflows

Amazon Web Services has launched incremental Machine Learning-based matching workflows for AWS Entity Resolution in general availability, addressing a significant scalability bottleneck that previously forced enterprises to reprocess entire datasets when adding new records. The enhancement allows businesses to process only newly added records since their last workflow run, reducing processing time by 95% and enabling 1 million incremental records to be processed in under one hour compared to the previous multi-day requirement. The new capability supports incremental workloads up to 50 million records over datasets containing up to 1 billion historical base records, making continuous large-scale entity resolution economically viable for enterprise deployments. Previously, adding even a single record required complete dataset reprocessing that could take up to two days and cost thousands of dollars, creating operational bottlenecks that forced organizations to seek alternative solutions or costly workarounds. The incremental ML workflows are now available across all AWS regions where Entity Resolution is offered, with AWS providing documentation and user guides for implementation. This release represents a fundamental shift in how enterprises can approach real-time data matching and deduplication workflows at cloud scale.