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Anomalo is a data quality monitoring platform that automates the monitoring of a data warehouse to make sure everything is as expected. Either in the cloud or as an in-VPC deployment, Anomalo uses machine-learning to determine the range of expected data for every table it monitors as well as distribution changes and changes in relationship, flagging possible anomalies and alerting the data team in real-time. Not only does this allow data engineers to identify missing, stale, or corrupt data as soon as it is detected, but it can save companies from lost revenue, reputation damage and excessive use of engineering resources. By adding Anomalo to the data stack, companies can fully trust the data they spend countless hours and dollars capturing, ensuring the resulting analysis is an accurate snapshot of the state of their database.