GoldenMatch

vgoldenflow-native-v0.11.0 scope: goldenflow-native Feature

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

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

Ships a compiled ABI3 native accelerator wheel for goldenflow 1.13.0

Full changelog

The compiled goldenflow-native accelerator wheel for goldenflow 1.13.0. Ships the
owned goldenflow-core kernels (identifiers, names, email, url, numeric,
categorical, address, text, and the fuzzy category_auto_correct build_canonical_map)
as an abi3 wheel: pip install goldenflow[native]. Byte-identical to the pure-Python
fallbacks (proven by the cross-surface parity corpus + pinned-vector tests).

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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.