Do You Know AWS Entity Resolution? Efficiently Identify Duplicate Records with Fuzzy Matching!

GitHub repository for this series: AWS Deep Cuts

What is AWS Deep Cuts?

AWS Deep Cuts is a technical series that digs deep into AWS services that most people don't know about — the latest services, niche features, or services that require advanced academic knowledge. Just like "deep cuts" in music (hidden gems that only dedicated fans know), we uncover the lesser-known corners of AWS.

These services often lack online resources, making it difficult for beginners to get started.

The AWS Deep Cuts series provides clear explanations including prerequisite knowledge and hands-on tutorials that anyone can reproduce by following step-by-step instructions.

What is Entity Resolution?

Entity Resolution (ER) is the process of identifying multiple records scattered across different data sources that refer to the same real-world entity (person, company, address, product, etc.) and consolidating them into a single unified record.

If you're unfamiliar with this concept, imagine your phone's contact list with duplicate entries for the same friend. One entry says "Mai Suzuki" (auto-imported from LINE with only a phone number), while another says "Suzuki Mai" (manually entered with only an email address).
Figuring out that these two entries refer to the same person and merging them — that's Entity Resolution.

In the example above, you can't determine whether two records represent the same person by simply checking for an exact name match. Entity Resolution often involves cases where purely mechanical, rule-based processing falls short. That's why modern approaches leverage machine learning to perform smarter, more sophisticated matching — mimicking the human intuition of "these are probably the same person."

What is AWS Entity Resolution?

AWS Entity Resolution is a fully managed service that performs entity resolution at scale. It reduces the manual effort traditionally required for record matching while enabling human-level sophistication.

Key features include:

• Multiple matching workflows — Choose from rule-based, ML-based, or data service provider-based (LiveRamp, UID2, etc.) matching workflows.
• Advanced matching techniques — Fuzzy matching and ML-based methods can link similar records even with spelling mistakes or formatting variations.
• Near real-time matching — Use the Generate Match ID API to enable rule-based near real-time matching and generate corresponding Match IDs.
• Security and data residency — Data is encrypted by default with AWS-managed keys, with optional KMS key support. PrivateLink access from within a VPC is also available.
• Pricing model — Rule-based/ML-based matching costs $0.25 per 1,000 records; data service provider matching costs $0.10 per 1,000 records (provider subscription fees apply separately).

Common Use Cases

• Customer 360 / Marketing — Unify customer data across CRM, purchase history, and web behavior to build a single customer view.

Customer 360 refers to integrating all customer data an organization holds (attributes, purchase history, support interactions, web behavior, etc.) to gain a comprehensive, 360-degree understanding of each customer.

• Financial Services — Link transactions across multiple accounts to detect suspicious patterns and prevent fraud.
• Healthcare — Consolidate patient records distributed across hospitals and clinics to ensure treatment continuity.
• Retail / E-commerce — Combine purchase history and browsing behavior to improve recommendation and targeting accuracy.
• Data Cleansing — Remove duplicate records as a preprocessing step for ML model training or BI analysis.

Best Practices

• Thorough preprocessing — Standardize data formats and fill missing values to improve match accuracy.
• Cost management — Since billing is proportional to processed records, filter out unnecessary records before processing.
• Privacy protection — Hash or encrypt PII wherever possible, and enforce strict access controls and KMS key management.

Hands-On Tutorial

Check out the full hands-on walkthrough in our GitHub repository:
👉 AWS Entity Resolution Hands-On

Summary

AWS Entity Resolution is a powerful solution for organizations struggling with data duplication and formatting inconsistencies. Leverage its pre-built algorithms and flexible rule configurations to dramatically reduce the effort required for data integration!

References

1. AWS Entity Resolution Documentation
2. AWS Entity Resolution User Guide
3. AWS Entity Resolution Features