Streamlining Entity Resolution on AWS with Amazon Q and Senzing

Entity resolution represents one of the most critical yet challenging aspects of modern data architecture. Organizations across industries struggle to accurately identify and link records representing the same real-world entities across disparate data sources. Whether building anti-money laundering (AML) systems, know-your-customer (KYC) processes, master data management (MDM) platforms, or fraud detection systems, the fundamental challenge remains consistent: determining entity relationships within complex, inconsistent datasets.

Traditional approaches to entity resolution have required substantial investments in custom development, extensive tuning, and ongoing maintenance. Organizations have spent millions of dollars and years of development time building solutions that often fall short of production requirements. The combination of Amazon Q Developer  and Senzing AI for Entity Resolution on AWS  marks a major shift in entity resolution capabilities on AWS, delivering enterprise-grade accuracy and performance.

Data mapping – defining how source system fields correspond to the Senzing generic entity specifications (JSON)  – has historically been one of the most tedious aspects of implementation. As schemas grow more complex, the increasing number of entity relationships and validation steps make the mapping process significantly more involved.

Amazon Q Developer introduces a structured, repeatable workflow that combines AI assistance with human decision-making. The Senzing Mapping Assistant  guides developers through a five-stage process:

1. INIT: Set up your mapping project and understand the workflow

2. INVENTORY: Automatically analyze source data structure and fields

3. PLANNING: Receive AI-guided recommendations based on Senzing best practices

4. MAPPING: Generate production-ready mapper code automatically

5. OUTPUTS: Validate mappings using professional tools before loading data

This workflow provides AI-assisted decision-making while keeping you fully in control. Amazon Q Developer reads your data structure, understands Senzing specifications, and guides you through mapping decisions, but you make the final choices based on your understanding of the data.

Senzing® entity resolution is built on proven AWS services:

· Amazon EC2 or AWS Fargate provides compute infrastructure for the entity resolution engine

· Amazon SQS handles JSON message flows

· Amazon RDS (PostgreSQL) offers managed database storage for results and metadata

· Amazon S3 serves as the primary data lake for raw and processed entity data

· AWS Key Management Service (KMS) ensures data encryption in transit and at rest

· AWS Identity and Access Management (IAM) provides fine-grained access control

· Amazon VPC delivers network isolation and security groups

Senzing AI brings pre-trained, self-tuning algorithms that eliminate manual tuning. The solution adapts to new data patterns through feature engineering, dynamic threshold optimization, real-time learning, and multi-language/multi-character support.

Want to see this in action? We've built a hands-on workshop that teaches you how to leverage Amazon Q Developer for the entire entity resolution workflow, from mapping source data through analyzing resolution results through conversational AI.

· Map real source data to Senzing format using the AI-assisted 5-stage workflow

· Generate production-ready mapper code and documentation automatically

· Validate mappings using proven Senzing tools

· Load data and explore entity resolution results

· Master a repeatable workflow you can apply to your own data sources

The workshop includes two hands-on exercises: Customer Data Mapping (CSV format) and Watchlist Data Mapping (complex JSON containing international names and relationships). Each follows a structured workflow from examining source data through analyzing resolution results.

Who Should Attend: Software developers, data engineers, and technical professionals working with data integration and ETL workflows. Basic familiarity with IDEs and data formats is required, with no prior Senzing experience needed.

· Duration: Self-paced hands-on modules (2-3 hours total)

· Cost: Free for AWS-hosted events

· Environment: Cloud-based IDE with all software and sample code provided

A Total Economic Impact™ (TEI) study by Forrester Consulting  commissioned by Senzing, found that the composite organization achieved:

· 226% Return on investment (ROI)

· 95% reduction in time to add a new data source

· Payback: <6 months

· 25% reduction in incidents caused by mismatches

Join the Workshop to experience AI-assisted entity resolution firsthand with realistic data examples and learn a repeatable workflow.

AWS Marketplace Deployment: Senzing AI for entity resolution is available through AWS Marketplace with streamlined procurement, integrated billing, and support.

The combination of Amazon Q Developer  and Senzing AI for entity resolution on AWS  represents a fundamental shift in how organizations approach entity resolution challenges. By leveraging AWS's cloud platform with Senzing advanced AI capabilities, organizations can achieve production-ready entity resolution in weeks rather than years.

The future of entity resolution is cloud-native, AI-powered, and available today on AWS. Organizations that embrace this approach will gain significant competitive advantages through improved data quality, faster implementation cycles, and reduced operational costs.

Get started today by joining our hands-on workshop , or explore Senzing on AWS Marketplace  for enterprise-ready deployment with the security, scalability, and reliability that AWS customers expect.

Contributors:

Tina Myers is a Partner Solutions Architect for AWS, enabling consulting and technology partners to build and scale their AWS practices through technical enablement, architectural guidance, and strategic partnership development, helping partners deliver exceptional customer outcomes while driving AWS adoption.

Daryl Cartwright is a Partner Solutions Architect at AWS, working at the intersection of generative AI and partner enablement to guide partners through architectural design, responsible AI implementation, and go-to-market strategies that scale their AI practice growth.

Jeff Jonas is a data scientist and creator of entity resolution systems. For more than three decades, he has been at the forefront of solving complex big data problems for companies and governments and was recognized by National Geographic as the Wizard of Big Data.

Aaron is the Lead of Partnerships with AWS and is driving strategic collaborations that integrate Senzing advanced entity resolution technology with AWS's cloud ecosystem. Aaron believes Senzing and Amazon Q will transform the landscape of intelligent data processing.

Jeff Butcher is a founding team member of Senzing and is the original developer of the first commercially available entity resolution software.