Skip to content

GoldenMatch

v1.4.2 Feature

This release adds 1 notable feature for engineering teams evaluating rollout.

Published 3mo Data Pipelines & ETL
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

data-cleaning data-engineering data-matching data-quality deduplication entity-resolution
+14 more
fellegi-sunter fuzzy-matching knowledge-graph llm master-data-management mcp-server polars pprl python record-linkage rust splink typescript zero-config

Summary

AI summary

Auto-config compounds geo columns with name columns to prevent cross‑region false positives.

Full changelog

Geo-compound blocking in auto-config

Auto-config now compounds geo columns (state, city, county) with name columns in blocking keys. This prevents cross-region false positives where entities with the same name in different states were incorrectly matched.

What changed

  • When auto-config builds name-based blocking keys, it checks for available geo columns and compounds them with the name column
  • Picks the geo column that reduces max block size the most while staying under the safe block limit
  • Guards against None from empty group_by().max() results

Impact

On the CMS Hospital General Information dataset (5,426 records), this eliminates cross-state false positives where "MEMORIAL HOSPITAL" in IL was being matched with "MEMORIAL HOSPITAL" in IN.

Install

pip install goldenmatch==1.4.2

Full Changelog: https://github.com/benzsevern/goldenmatch/compare/v1.4.1...v1.4.2

View diff on GitHub

Weekly OSS security release digest.

The CVE patches and breaking changes that affected production tools this week. One email, every Sunday.

No spam, unsubscribe anytime.

Share this release

Share on X Share on Bluesky

Track GoldenMatch

Get notified when new releases ship.

Sign up free

About GoldenMatch

All releases →

Related context

Related tools

Earlier breaking changes

  • v3.1.0 `GOLDENMATCH_FRAME=polars` now requires the `[polars]` extra; raises error without it.
  • vgoldencheck-v3.0.0 `inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.
  • vgoldencheck-v3.0.0 'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.
  • vgoldencheck-v3.0.0 `scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.
  • v3.0.0 Result frames now return pyarrow.Table instead of Polars DataFrame.

Beta — feedback welcome: [email protected]