Run Zingg on AWS Glue or Amazon EMR, reading from and writing to Amazon S3. Your data stays in your AWS account, and every resolved entity gets a persistent ZINGG_ID.
Runs on the Spark services you already use on AWS.Run Zingg in AWS Glue for entity resolution and fuzzy matching. AWS Big Data blog
Submit Zingg as a Spark step on an EMR cluster, or run the Zingg Python API in EMR notebooks.
Zingg reads source data from S3 and writes resolved entities back, all inside your AWS account.
A set of notebooks on the Zingg Python API that take you from setup to labeling training pairs, model documentation, and training and matching, on AWS Glue or EMR notebooks with S3 as source and sink.
Zingg notebooksIncremental flow, persistent ZINGG_IDs, explainability and model diff, plus support from the builders of Zingg.
Talk to usA persistent, global identifier for every resolved entity, so every team and agent joins on the same key.
Learn moreMatch only new, updated and deleted records against the clusters already resolved. IDs persist between runs.
Learn moreSee the evidence behind every match, so you can explain results to business users.
Learn moreConfidently add new signals, update configuration and deploy models in production.
Learn moreYes. Zingg runs in AWS Glue for entity resolution and fuzzy matching; the AWS Big Data blog walks through it.
Yes. Submit Zingg as a Spark step on an EMR cluster with your config and phase, or run the Zingg Python API in EMR notebooks.
Yes. S3 can be both the source of your records and the destination for resolved entities.
No. Zingg runs on Glue or EMR in your account and writes results back to your storage.
Run Zingg on Glue or EMR in your own AWS account.