Anomalo

Anomalo is an AI-powered data quality and observability tool that continuously monitors your company’s data tables for unexpected issues. Instead of requiring you to write manual data validation rules, Anomalo’s machine learning algorithms learn the normal patterns in your data and flag anomalies automatically. For example, Anomalo can detect if a daily sales table suddenly has a drastic drop (or spike) in volume, if certain categories of data are missing or skewed, or if a relationship between two columns changes significantly.

It integrates with modern data warehouses like Snowflake, Redshift, BigQuery, Databricks and others – you simply connect Anomalo to your data source and select which tables to monitor. The system then generates a baseline of what “normal” data looks like (taking into account seasonality, trends, etc.) and starts issuing alerts when it finds issues, often pointing to potential causes.

For each anomaly, Anomalo provides a report and visualization – for instance, highlighting that “records from X region are 90% lower than usual” or “this column has 30% null values today vs. <1% normally”. It also helps with root cause analysis by correlating anomalies across tables (e.g., an upstream data pipeline failure). Data teams and analytics engineers use Anomalo to ensure the accuracy of dashboards, machine learning inputs, and reports by catching data problems early.

Anomalo typically is offered as a SaaS platform (and since being acquired by dbt Labs, may integrate with dbt’s data transformation workflows). Pricing is enterprise-grade (usually based on number of tables or data volume). By implementing Anomalo, organizations gain trust in their data with minimal manual rule-writing, letting AI guard the quality of their critical datasets 24/7.