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Zero-config entity resolution & record linkage. The zero-tuning Fellegi-Sunter path beats hand-tuned Splink head-to-head and scales from a CSV to a verified 100M-row dedupe in 9.2 min. Fuzzy/exact/probabilistic + PPRL + LLM + identity graph. Python + edge-safe TypeScript (WASM), SQL-native in Postgres & DuckDB, MCP/REST + dbt/Airflow.

125 starsPython

GoldenMatch – 100M-row dedupe on Ray in 213s, no Spark, Arrow-native

by benzsevern·Jun 4, 2026·3 points·0 comments
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AI Analysis

●●SolidBig BrainShip It

Ray-based dedupe at 100M rows without Spark — that's a real architectural choice.

Strengths
  • •0.30 GB driver footprint while processing 100M records is genuinely impressive.
  • •Polyglot support across Python, TypeScript, PostgreSQL, and DuckDB.
  • •MCP servers and dbt recipes show production integration thinking.
Weaknesses
  • •Entity resolution already has established players like Splink and Dedupe.io.
  • •74 stars suggests early adoption — real-world scale validation still pending.
Category
Data
Target Audience

Data engineers, data scientists

Similar To

Splink · Dedupe.io · OpenRefine

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