GoldenMatch

vgolden-suite-v0.1.9 scope: golden-suite Breaking

This release includes 3 breaking changes for platform teams planning a safe upgrade.

Published 18d Data Pipelines & ETL

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✓ No known CVEs patched

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✓ 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

Affected surfaces

deps breaking_upgrade

Summary

AI summary

Bumps minimum goldenflow version to >=2.0.0 and goldenflow‑native to >=0.26.0, moving Polars support to an optional extra.

Full changelog

Lockstep with goldenflow 2.0.0 (Polars eviction complete). Bumps the goldenflow floor to >=2.0.0 and goldenflow-native to >=0.26.0. goldenflow 2.0.0 is Polars-free by default (Polars moved to the goldenflow[polars] extra; goldenflow-native is a base dep). Deps-only meta-package.

Breaking Changes

Minimum required goldenflow version raised to >=2.0.0 (Polars eviction)
Minimum required goldenflow‑native version raised to >=0.26.0
Polars support moved from core to the goldenflow[polars] extra

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.