Anomalo
Automated data quality monitoring with machine learning
Anomalo is profiled here as a Data Quality tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Anomalo is a data quality platform, founded in 2018 by Elliot Shmukler and Jeremy Stanley, that monitors warehouse tables for problems without requiring teams to write rules for every check. It connects to a warehouse, learns the normal shape of each table, and uses machine learning to flag anomalies in volume, freshness, and distribution, then explains which rows and segments drove a change. Teams add targeted validation rules where they need them, and Anomalo routes alerts so data issues surface before they reach dashboards and models.
Key Capabilities:
Machine-learning anomaly detection that needs no hand-written rules
Automatic monitoring of volume, freshness, and schema changes
Root-cause analysis that isolates the rows and segments behind a change
Custom validation rules and checks for specific business logic
Alert routing to Slack, email, and incident tools
Connectors for Snowflake, BigQuery, Databricks, and Redshift
Alternative tools
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- Elementary
dbt-native data observability and anomaly detection
- Soda
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- GX (Great Expectations)
Declarative data quality testing for pipelines