rbmuller/scherlok

Overview

A zero-configuration data quality monitoring tool for SQL-based data warehouses. It connects to dbt, Postgres, BigQuery, or Snowflake and automatically detects anomalies without requiring YAML configuration.

Best for

Best for
Developers and data engineers using dbt who need quick, automated data quality checks without manual configuration.

Use cases

• Detect unexpected schema changes in production tables
• Monitor data freshness and row count anomalies
• Automatically flag outlier values in numeric columns

Notes

A zero-configuration data quality monitoring tool for SQL-based data warehouses. It connects to dbt, Postgres, BigQuery, or Snowflake and automatically detects anomalies without requiring YAML configuration.

6 stars on GitHub. Last updated 2026-06-05. Licensed MIT.

Use cases

• Detect unexpected schema changes in production tables
• Monitor data freshness and row count anomalies
• Automatically flag outlier values in numeric columns

Pros

• No configuration files required
• Integrates directly with dbt and major data warehouses
• Lightweight Python-based setup

Cons

• Limited customization of monitoring rules
• Only supports SQL-based warehouses
• Small community and low star count

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