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Enterprise Layer Intelligence
Categories/Data Platform & Integration/Zingg.AI
Zingg.AI

Zingg.AI

Founded 2021

Resolve duplicate customer entities and build golden records across your data warehouses

You can use Zingg to unify records and perform entity resolution directly inside your modern data warehouse. It connects with systems like Snowflake, Databricks, and Google Cloud Platform to identify duplicate records without manual rule writing. By labeling a small set of matching pairs, Zingg learns matching patterns to connect people across nicknames and organizations across abbreviations. It assigns persistent identifiers to each entity, automates fuzzy matching at scale, and keeps customer profiles up to date without requiring complete database reprocessing.

What Zingg.AI does

Label record pairs to train custom machine learning matching modelsGenerate unique identity clusters and golden recordsConnect data warehouse tables to entity resolution pipelinesMatch person profiles using common nickname dictionariesResolve company records using organizational abbreviation logicAppend persistent unique identifiers to source database tablesRuns natively inside data warehouses like Snowflake and DatabricksTrains matching models using active learning with 40-50 labeled pairsAssigns persistent, globally unique identifiers across all recordsPerforms fuzzy matching on nicknames and company abbreviationsResolves updated and incoming records without full reprocessingScales matching across millions of records without pairwise comparisons

Frequently asked

DatabricksDatabricksSnowflakeSnowflake

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Zingg, master data management, entity resolution, golden record, deduplication, fuzzy matching, Snowflake, Databricks, open source MDM, identity graph, customer 360, data integration, active learning, data warehouse native, persistent identifier