One matching engine. Reachable from anywhere.
A new REST API and containerized deployment lets matching run right inside your pipeline.
No credit card required.
10M
Records in 41 Minutes
REST
Any Language, Any Framework
2x
Faster Profiling
What's new in DataMatch Enterprise
Six changes, all running on the same engine you already know.
REST API
Matching, profiling, and data preparation are now callable over standard REST. Any language or framework that can make an HTTP request can integrate directly, so matching becomes a step inside an existing pipeline instead of a separate task someone hands off.
Docker-based, platform-agnostic deployment
Matching, profiling, and data preparation are now callable over standard REST. Any language or framework that can make an HTTP request can integrate directly, so matching becomes a step inside an existing pipeline instead of a separate task someone hands off.
Entity graphs
Match results can now be explored as an interactive graph showing how records connect and how a matched group was formed, so complex or chained groupings are visible at a glance instead of reconstructed from a table.
Live search
Live search matches in real time, up to 200 records a second. Search your data and get matched results back immediately, no need to configure or run a full job first.
Profiling export
Profiling results can now be exported out of the platform, so data quality findings can be attached to reports or shared with teams who don't use DataMatch Enterprise directly.
Rebuilt numeric & phonetic matching
Numeric matching now runs on range-based or percentile-based comparison instead of exact-match string logic. Phonetic matching splits into exact and fuzzy modes built to catch name variants that differ in spelling and sound.
Profiling, redesigned
Rebuilt around how you actually work
Profiling is rebuilt around a column-oriented view that surfaces more detail about each field with less configuration to get there. Data preparation follows the same logic, cutting the trial and error that used to happen before a matching job could even start.
Cross-column matching, extended
More control over how fields compare against each other when data is structured inconsistently across sources.
Redesigned interface
Navigation, configuration, and results review follow a consistent structure, so options show up where you need them instead of buried in dialogs.
Benchmarks
Execution time, side by side
Head-to-head timing from internal testing across import, profiling, and matching, on the same machine and the same data.
Data import
Data profiling
Matching, 10M records
On the 10 million record run, the new web app returned 117,201 matched groups across 439,549 pairs, a scale the desktop architecture wasn’t designed to process.
Architecture
The UI and the API run on the same engine
Most vendors ship the no-code interface and the developer API as two different products, often on two different data models. DataMatch Enterprise runs both on the same engine, so nothing gets lost moving between them.
| Typical setup | DataMatch Enterprise | |
|---|---|---|
| Interface | UI and API are usually separate products | Same engine behind the code-free UI and the REST API |
| Switching tools | Moving from UI to API often means starting over | Add API access without giving up the interface your team already knows |
| Match review | Built for one audience, technical or not, rarely both | Entity graphs and match review available either way you connect |
| Team access | Every change routes through whoever owns the integration | Data teams configure and review directly; engineering plugs in independently |
Already using DataMatch Enterprise?
This release is available now. Reach out to your account team for upgrade details specific to your deployment.