When integrating disparate datasets, data scientists must often perform “entity resolution” to link records related to the same person or organization. Entity resolution systems that don’t handle ambiguous records well can lead to poor analysis and decision-making. To facilitate entity resolution, Zingg has OSS’ed their no-code ML-based tool. Zingg, which runs on Spark, connects to on-premise and cloud data sources and learns a blocking model that indexes near similar records and a similarity model to predict matching pairs. It can write unified views of entities to Snowflake, Cassandra, S3, Elastic, and other major RDBMS.