Continuously Updated Identity Graph Right Within Your Datalake And Warehouse

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Use Databricks with Zingg for AI-driven entity resolution, data deduplication, and identity matching, ensuring a single source of truth for customer and enterprise data. Leverage Snowflake for AI-powered entity resolution with Zingg, enabling deduplication, identity matching, and a unified customer view for cleaner, more reliable data. Unify and deduplicate customer records in Microsoft Fabric with AI-powered entity resolution for accurate, high-quality data. Harness the power of AWS with Zingg for scalable entity resolutionOptimize Amazon Redshift with Zingg’s AI-powered entity resolution, enabling identity resolution, deduplication, and a single source of truth for master data management and customer 360.

Zingg Entity Resolution

Unified customers, locations, suppliers and products using AI

TrainPrivately On Your Own Data By Answering Yes. No. Cant Say

Label 40-50 Matching Pairs To Train Zingg AI To Match On Fields Of Your Choice.

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Zingg ID vs traditional entity resolution: AI-powered accuracy and scalability.
zingg’s AI-driven entity resolution to handle millions of records across Snowflake, Databricks, BigQuery, AWS Glue, and Microsoft Fabric, ensuring fast, accurate, and efficient data deduplication.

Scale Rapidly To MillionsOf Records

Zingg's AI Learns Not To Compare Every Record With Every Other Record.

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Cross Reference Every Record Across Every System

ZINGG_ID is the globally unique and persistent identifier you always needed but never had.

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Zingg ID supports deterministic and probabilistic matching for AI-driven entity resolution

Resolve Updated And New Records Without Full Reprocessing

Zingg automatically updates the identity graph with new and updated entities.

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Match On Nicknames

Identify people with nickname variations.

Transform And Match

Save valuable time massaging data

Trusted By Data Leaders Globally!

Connect fragmented customer records in Databricks with Zingg’s entity resolution.

Jonathan Moss, Director of Customer Engagement,
Fortnum & Mason

We are able to, for the first time, understand how customers are shopping with us, whether it be online, in-store, over the phone, or in restaurants. We have never been able to do that properly before.

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Unify enterprise data with Zingg’s single source of truth on AWS Glue.

Shelomo Dobkin, Director of Product Development,
‍Orthodox Union

We moved from using Zingg Open Source to the enterprise version on Snowflake, making it the engine powering our golden records. Zingg’s powerful features and clean results have really set us apart.

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zingg automates deduplication for cleaner customer data in BigQuery.

Arijit Saha, Chief Technology Officer,
Redica Systems

Compared to the previous approach, it’s obviously much better—probably more than 90% better.

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Resolve identities across GCP & CDP for accurate customer profiles with Zingg.

Dave Musambi, Senior Director, Business Intelligence,
‍Canadian Football League

Zingg provides a sophisticated solution for fuzzy matching that is crucial in industries like sports, where dirty data is common. Thanks to that, we’ve seen 10-15% of our records successfully consolidate into one profile—this has been a huge win for us.

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