Incremental Flow For Identity Resolution

Zingg maintains the identity graph with resolved entities as records are added, updated and deleted

Zingg Merges Previously Resolved Clusters With New Signals In The Data

Zingg Matches Records Based On Existing Signals

Poorly populated or third party systems generally have less information to identify entities

Cloud-based entity resolution with Zingg ID for scalable customer identity management

New Information Gets Added To The Record

Over time, records get updated due to improvements in systems and preprocessing and transformation logic

AI-powered golden records creation with Zingg ID for data accuracy and compliance.

Zingg updates the resolved identities

Updated records present new opportunities to match with existing entities

Zingg ID leverages machine learning for precise entity resolution and data deduplication

Zingg Breaks Previously Resolved Clusters When New Data Has Different Signals

Zingg Matches Records Based On Existing Information

Poorly or erroneously populated records and third party get resolved based on existing attributes

Zingg ID helps businesses achieve GDPR compliance with AI-driven entity resolution

Records Get Updated And Corrected

New feeds, improved transformation and data processing logic cleans up errors

Open source entity matching pipeline built natively on Databricks

Zingg Breaks Such Clusters And Assigns New Zingg IDs

The identity graph is updated and maintained with every available bit of information

Incremental entity resolution with Zingg ID ensures continuously updated and clean data.
Incremental Flow Documentation
Incremental Flow Deep Dive

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