A simple look at how AWS Entity Resolution matches related records across different datasets and helps create a more consistent view of data.

Learn how to achieve a perfect golden record for entity resolution using graph data models, AWS, and Aerospike for enhanced ad targeting and personalization.

Learn how to achieve a perfect golden record for entity resolution using graph data models, AWS, and Aerospike for enhanced ad targeting and personalization.

Eyal Estrin unread, 4:16 AM (10 minutes ago)    to https://aws.amazon.com/blogs/industries/measuring-the-accuracy-of-rule-or-ml-based-matching-in-aws-entity-resolution/ Eyal Estrin CISSP, CCSP, CISM, CISA, CDPSE, CCSK Blog: https://security-24-7.com | Books: https://amzn.to/42Xai9A | https://amzn.to/3Sggbtv Twitter: @eyalestrin | Bluesky: @eyalestrin.bsky.social

Today, AWS Entity Resolution announces advanced rule-based fuzzy matching using Levenshtein Distance, Cosine Similarity, and Soundex algorithms to help organizations resolve consumer records across fragmented, inconsistent, and often incomplete datasets. This feature introduces tolerance for variations and typos, enabling potentially more accurate and flexible entity resolution without requiring the manual pre-processing of records. Advanced rule-based fuzzy matching in AWS Entity Resolution helps customers improve match rates, enhance personalization, and unify consumer views, critical for effective cross-channel targeting, retargeting, and measurement. AWS Entity Resolution advanced rule-based fuzzy matching bridges the gap between traditional rule-based and machine learning-based matching techniques. Customers can use fuzzy algorithms to set similarity, distance, and phonetic thresholds on string fields to match records, offering the configurability of deterministic matching with the flexibility of probabilistic matching. This feature can be applied across multiple industries including advertising and marketing, retail and consumer goods, or financial services, where resolving consumer records are critical for verifying customers, fraud detection, or marketing purposes. AWS Entity Resolution helps organizations match, link, and enhance related customer, product, business, or healthcare records stored across multiple applications, channels, and data stores. You can get started in minutes using matching workflows that are flexible, scalable, and can seamlessly connect to your existing applications, without requiring any expertise in entity resolution or ML. AWS Entity Resolution is generally available in these https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/. To learn more, visit https://aws.amazon.com/entity-resolution/.

Today, AWS Entity Resolution announces advanced rule-based fuzzy matching using Levenshtein Distance, Cosine Similarity, and Soundex algorithms to help organizations resolve consumer records across fragmented, inconsistent, and often incomplete datasets. This feature introduces tolerance for variations and typos, enabling potentially more accurate and flexible entity resolution without requiring the manual pre-processing of records. Advanced rule-based fuzzy matching in AWS Entity Resolution helps customers improve match rates, enhance personalization, and unify consumer views, critical for effective cross-channel targeting, retargeting, and measurement. AWS Entity Resolution advanced rule-based fuzzy matching bridges the gap between traditional rule-based and machine learning-based matching techniques. Customers can use fuzzy algorithms to set similarity, distance, and phonetic thresholds on string fields to match records, offering the configurability of deterministic matching with the flexibility of probabilistic matching. This feature can be applied across multiple industries including advertising and marketing, retail and consumer goods, or financial services, where resolving consumer records are critical for verifying customers, fraud detection, or marketing purposes. AWS Entity Resolution helps organizations match, link, and enhance related customer, product, business, or healthcare records stored across multiple applications, channels, and data stores. You can get started in minutes using matching workflows that are flexible, scalable, and can seamlessly connect to your existing applications, without requiring any expertise in entity resolution or ML. AWS Entity Resolution is generally available in these AWS Regions. To learn more, visit AWS Entity Resolution.

AWS Clean Rooms now supports incremental processing of rule-based ID mapping workflows with AWS Entity Resolution. This helps you perform real-time data synchronization across collaborators’ datasets with the privacy-enhancing controls of AWS Clean Rooms. With this launch, you can populate ID mapping tables in a Clean Rooms collaboration with only the new, modified, or deleted records since the last analysis. Data collaborators can enable incremental processing for rule-based ID mapping workflows in AWS Entity Resolution, and then update an existing ID mapping table in a collaboration. For example, a measurement provider can maintain up-to-date offline purchase data in a collaboration with an advertiser and a publisher, enabling always-on measurement of campaign outcomes, reduced costs, and maintained privacy controls for all collaboration members. AWS Entity Resolution is natively integrated within AWS Clean Rooms to help you and your partners more easily prepare and match related customer records. Using rule-based or data service provider-based matching can help you improve data matching for enhanced advertising campaign planning, targeting, and measurement. For more information about the AWS Regions where AWS Clean Rooms is available, see the https://docs.aws.amazon.com/general/latest/gr/clean-rooms.html#clean-rooms_region table. To learn more about AWS Clean Rooms, visit https://aws.amazon.com/clean-rooms/.

AWS Clean Rooms now supports incremental processing of rule-based ID mapping workflows with AWS Entity Resolution. This helps you perform real-time data synchronization across collaborators’ datasets with the privacy-enhancing controls of AWS Clean Rooms. With this launch, you can populate ID mapping tables in a Clean Rooms collaboration with only the new, modified, or deleted records since the last analysis. Data collaborators can enable incremental processing for rule-based ID mapping workflows in AWS Entity Resolution, and then update an existing ID mapping table in a collaboration. For example, a measurement provider can maintain up-to-date offline purchase data in a collaboration with an advertiser and a publisher, enabling always-on measurement of campaign outcomes, reduced costs, and maintained privacy controls for all collaboration members. AWS Entity Resolution is natively integrated within AWS Clean Rooms to help you and your partners more easily prepare and match related customer records. Using rule-based or data service provider-based matching can help you improve data matching for enhanced advertising campaign planning, targeting, and measurement. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about AWS Clean Rooms, visit AWS Clean Rooms.

Enterprises often struggle with fragmented and inconsistent consumer data spread across multiple applications. This can create challenges in achieving trusted, unified views of your customers that power important functions, such as compliance reporting, analytics, and consumer engagement. Amazon Web Services (AWS) Entity Resolution helps companies quickly match, link, and enhance related customer records so organizations [...]

Today, AWS Entity Resolution introduces near real-time rule-based matching to enable customers to match new and existing records within seconds. With this launch, organizations can match records in near real-time to support use cases across multiple industries that require low-latency, time-sensitive matching on consumer records. For example, travel and hospitality companies can match consumer records in near real-time to prioritize callers in contact centers, recognize loyal customers, deliver tailored product recommendations, and personalize guest check-in experiences. Healthcare organizations can match patient records in near real-time, with appropriate patient consent, to provide clinicians with a complete view of medical history and improve care coordination. Additionally, financial institutions can match new and historical financial transactions within seconds to identify discrepancies and detect fraudulent transactions. When processing incremental records using rule-based matching, AWS Entity Resolution compares the incoming record against existing records, returns a match using a consistent match ID, or https://docs.aws.amazon.com/entityresolution/latest/userguide/generate-match-id.html, all within seconds. AWS Entity Resolution helps organizations match, link, and enhance related customer, product, business, or healthcare records stored across multiple applications, channels, and data stores. You can get started in minutes using matching workflows that are flexible, scalable, and can seamlessly connect to your existing applications, without any expertise in entity resolution or ML. AWS Entity Resolution is generally available in these https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/. To learn more, visit https://aws.amazon.com/entity-resolution/.

How do you know that an entity matching ruleset or model is actually accurate enough? Whether evaluating multiple identity providers or building your own matching rules, companies need to establish clear, accuracy level criteria they want to achieve, as well as the framework to measure and compare different approaches objectively. Companies who do not measure [...]

Simplify your DynamoDB Typescript code with DynamoDB Entity store

Companies across industries aim to deliver personalized customer experiences and optimized advertising campaigns by resolving customer identities. However, being able to reliably match records across fragmented, inconsistent, and often messy datasets can be hard and complicated. Organizations using traditional rule-based matching techniques to help match exact records often miss valuable connections due to slight variations [...]

Description  Entity resolution supports almost every modern data system from AML compliance and KYC to fraud detection, customer data platforms, and suppl...

    AWS Entity Resolution adds record-level confidence scores for ML matching 0 views Eyal Estrin unread, 12:17 PM (23 minutes ago)    to https://aws.amazon.com/about-aws/whats-new/2026/09/entity-resolution-record-confidence/ https://docs.aws.amazon.com/entityresolution/latest/userguide/create-matching-workflow-ml.html Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

AWS Entity Resolution now provides record-level confidence scores for ML-based matching, enabling differentiated thresholds and larger addressable audiences.

AWS Entity Resolution now provides record-level confidence scores for Machine Learning (ML) based matching workflows, giving you a per-record signal of how confident the model is in each individual identity match. Previously, all records within a match group carried the same group-level confidence score regardless of actual match quality — making it impossible to distinguish a near-certain match from a borderline one. This forced customers to apply a single confidence threshold across all records, limiting the number of resolved identities that could be activated downstream. With record-level confidence scores, each resolved record now carries its own score reflecting actual match quality. This gives you the precision to qualify more records for activation by applying differentiated thresholds — using higher confidence for automated merges and lower thresholds to include additional records that still meet quality standards. The result is larger addressable audiences and improved lead conversions, while maintaining compliance-grade match transparency with audit-ready evidence for each resolved record. For incremental ML workflows, the existing RecordConfidenceLevel column now reflects the actual per-record score with no schema changes required. You can start using record-level confidence scores in all https://aws.amazon.com/entity-resolution/faqs/ where AWS Entity Resolution is available. For more information, see our https://docs.aws.amazon.com/entityresolution/latest/userguide/create-matching-workflow-ml.html. For more information about AWS Entity Resolution, visit our https://aws.amazon.com/entity-resolution/.

AWS Entity Resolution now provides record-level confidence scores for Machine Learning (ML) based matching workflows, giving you a per-record signal of how confident the model is in each individual identity match. Previously, all records within a match group carried the same group-level confidence score regardless of actual match quality — making it impossible to distinguish a near-certain match from a borderline one. This forced customers to apply a single confidence threshold across all records, limiting the number of resolved identities that could be activated downstream. With record-level confidence scores, each resolved record now carries its own score reflecting actual match quality. This gives you the precision to qualify more records for activation by applying differentiated thresholds — using higher confidence for automated merges and lower thresholds to include additional records that still meet quality standards. The result is larger addressable audiences and improved lead conversions, while maintaining compliance-grade match transparency with audit-ready evidence for each resolved record. For incremental ML workflows, the existing RecordConfidenceLevel column now reflects the actual per-record score with no schema changes required. You can start using record-level confidence scores in all AWS Regions where AWS Entity Resolution is available. For more information, see our user guide. For more information about AWS Entity Resolution, visit our product page.

AWS Entity Resolution now provides record-level confidence scores for Machine Learning (ML) based matching workflows, giving you a per-record signal of how confident the model is in each individual identity match. Previously, all records within a match group carried the same group-level confidence score regardless of actual match quality — making it impossible to distinguish a near-certain match from a borderline one. This forced customers to apply a single confidence threshold across all records, limiting the number of resolved identities that could be activated downstream. With record-level confidence scores, each resolved record now carries its own score reflecting actual match quality. This gives you the precision to qualify more records for activation by applying differentiated thresholds — using higher confidence for automated merges and lower thresholds to include additional records that still meet quality standards. The result is larger addressable audiences and improved lead conversions, while maintaining compliance-grade match transparency with audit-ready evidence for each resolved record. For incremental ML workflows, the existing RecordConfidenceLevel column now reflects the actual per-record score with no schema changes required. You can start using record-level confidence scores in all AWS Regions where AWS Entity Resolution is available. For more information, see our user guide. For more information about AWS Entity Resolution, visit our product page.

Eyal Estrin unread, 4:56 AM (24 minutes ago)    to https://aws.amazon.com/about-aws/whats-new/2026/07/aws-entity-resolution/ https://docs.aws.amazon.com/entityresolution/latest/userguide/generate-match-id.html Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com