GoldenMatch

v1.3.1 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 3mo Data Pipelines & ETL

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✓ 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

Updates What's New, Safety, and validate across a mixed release.

Full changelog

What's New

GoldenFlow Integration

GoldenMatch now integrates with GoldenFlow for automatic data transformation before matching.

pip install goldenmatch[transform]

When GoldenFlow is installed, it runs automatically in the dedupe pipeline after GoldenCheck validation and before autofix:

ingest -> GoldenCheck (validate) -> GoldenFlow (transform) -> autofix -> standardize -> match

GoldenFlow handles format normalization that neither GoldenCheck nor GoldenMatch's autofix addresses:

Phone numbers (15 formats -> E.164)
Dates (MM/DD/YYYY vs YYYY-MM-DD -> ISO 8601)
Address abbreviations
Categorical misspellings (fuzzy matching)
Unicode normalization, smart quotes

Config

transform:
  enabled: true
  mode: announced  # "announced" | "silent" | "disabled"

Or zero-config: if goldenflow is installed, it runs automatically.

Safety

Graceful degradation: if goldenflow crashes, logs a warning and continues with untransformed data
Warns when config enables transforms but goldenflow is not installed
Uses logger (not print) for safe operation in REST/MCP/agent contexts

Full Changelog: https://github.com/benzsevern/goldenmatch/compare/v1.3.0...v1.3.1

View diff on GitHub

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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.