GoldenMatch

vgolden-suite-v0.1.3 scope: golden-suite Breaking

This release includes 2 breaking changes for platform teams planning a safe upgrade.

Published 21d 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

Summary

AI summary

Minimum goldenflow version bumped to 1.13.0 and goldenflow-native to 0.11.0.

Full changelog

Lockstep floor bump after the goldenflow Wave D owned-kernel release.

Changed:

goldenflow>=1.13.0 (was >=1.4.0) -- the owned-kernel + cross-surface migration of every byte-parity-achievable transform family is complete (identifiers, names, email, url, numeric, categorical, address, the full text family, and fuzzy category_auto_correct).
goldenflow-native>=0.11.0 (was >=0.2.0) -- the matching compiled-kernel wheel.

Deps-only meta-package; ships no logic of its own beyond the CLI + introspection helpers. Both floors are live on PyPI.

Breaking Changes

Minimum goldenflow requirement increased from >=1.4.0 to >=1.13.0
Minimum goldenflow-native requirement increased from >=0.2.0 to >=0.11.0

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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.