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GoldenCheck is pure-Python and Polars-native, so the everyday sampled scan is already fast. For the CPU-heavy deep-profiling checks there’s an optional compiled runtime, and the package falls back to pure Python when it isn’t installed — behaviour is identical either way, native only changes wall-clock. 
pip install goldencheck[native]
goldencheck.core._native_loader discovers the kernels automatically; nothing in your code changes. Control it with GOLDENCHECK_NATIVE=auto|0|1 (auto is the default: use native where available, fall back otherwise).

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How the kernels earn their place

Every kernel had to clear two gates before being switched on by default: it must be byte-identical / integer-exact to the pure-Python reference, and measurably faster than the Polars baseline on a real workload. (One early kernel was actually slower than Polars and was rewritten before shipping — the rule is “beat Polars”, not “it’s Rust”.) Checks where Polars already wins — duplicate-row detection, referential integrity, freshness — stay pure-Polars.
CheckSpeedupWhat it finds
Benford~16×leading-digit anomalies in amount/count columns
Composite-key discovery1.7×minimal multi-column keys when no single column is unique
Functional-dependency discovery12.8×zip → city-style redundant / lookup columns
Approximate-FD violations15.5×the few rows that break a near-perfect dependency (likely data-entry errors)
Fuzzy value clustering76×inconsistent categorical encodings (California / Californa / CALIFORNIA)

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New deep-profiling checks

  • Composite keys — surfaces (order_id, line_no)-style natural keys.
  • Functional dependencies — exact det → dep relationships (a column is derivable from another), and approximate ones where a handful of violating rows are flagged as likely errors.
  • Fuzzy values — near-duplicate spellings within a column.
  • Duplicate & near-duplicate rows — exact, and rows identical after normalization.
  • Freshness / staleness — future-dated timestamps (always on) and name-gated staleness (updated_at that hasn’t advanced).

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--deep — profile the full population

By default GoldenCheck samples large files to 100K rows. --deep profiles the entire dataset, removing sampling error on cardinality, uniqueness, and rare-value checks — the native kernels keep it affordable. 
goldencheck data.csv --deep

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refs — cross-file referential integrity

Validate that a child table’s foreign keys all exist in a parent’s key: 
goldencheck refs orders.csv customers.csv --on customer_id=id
Reports orphan rows, the orphan rate, and join cardinality; exits non-zero when orphans exist (CI-friendly). Omit --on to auto-detect same-named key columns.

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Quality signals for GoldenMatch

The native fuzzy + FD kernels also back two public APIs that GoldenMatch consumes for entity resolution: goldencheck.cell_quality(df) (per-cell quality) and goldencheck.functional_dependencies(df) (discovered FDs).