Tamr Product Updates: July 2026

July’s updates focus on giving teams more precise control over how records are matched, making duplicate review faster, and expanding the sources Tamr can read from. With updated match rules in real-time data products, a redesigned feedback experience on 360 pages, and native support for Apache Iceberg tables, these enhancements improve how teams manage, review, and connect trusted data.

Precise Control Over Matching With Match Rules

Match rules are now available in limited release for real-time data products. Rules deterministically identify records that should or should not be matched together, based on matching or non-matching values in the attributes you specify—giving you direct control over record resolution alongside Tamr’s machine learning models.

You can create three types of rules:

• Exact Match (Merge): Records with matching non-null values for the selected attributes are clustered together.
• Mismatch (Split): Records with different non-null values for the selected attributes are kept apart.
• Both: All records in a cluster must share either the same value or a null value for the selected attributes.

When multiple rules apply, those higher in the list take precedence. This is particularly useful when a trusted identifier—such as a customer number, a National Provider Identifier (NPI), or a supplier code—should always govern the outcome.

Faster Duplicate Review on 360 Pages

The new Feedback section on 360 pages replaces the previous Suggested Duplicates and Curation Activity sections, and is available across all data products with a System of Record. Everyone can now see open and closed curation items for a record in one place, review possible duplicates in detail, and suggest merges for further review. Users with curator or higher permission can also dismiss suggestions outright or move directly into Curator Hub—fewer clicks between spotting a duplicate and resolving it.

Support for Apache Iceberg Sources

You can now add Apache Iceberg sources through your S3, ADLS, and GCS connections. Teams standardizing on Iceberg-based lakehouse architectures can bring those tables into Tamr directly, without a separate ingestion path or intermediate file exports.

Coming Soon: Uniformity Scores

Uniformity scores (High, Medium, Low) will indicate how closely clustered source record values align with golden record values, helping curators identify clusters that need a second look. You’ll be able to configure an overall score for each cluster by choosing which attributes to include and calculate individual scores for specific attributes. Low uniformity is a useful early signal that a cluster may contain records representing more than one entity.

These July updates continue Tamr’s commitment to making data mastering more efficient, transparent, and reliable. From giving curators control over match outcomes to streamlining duplicate review and broadening source connectivity, these enhancements help organizations manage trusted data with greater speed and confidence.

Where to Learn More

Visit Tamr Docs for a comprehensive breakdown of all July product changes, including bug fixes, UI improvements, and known issues.