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The Problem

Every data team has the same nightmare:

A source API silently changes from dollars to cents. Revenue dashboards show wrong numbers for 3 weeks before anyone notices.

A column starts returning NULLs. A table stops updating. Row counts drop 40% on a Tuesday. Nobody knows until the CEO asks why the report looks weird.

Current tools (Great Expectations, Soda, dbt tests) require you to define what "correct" looks like before you can detect what's wrong. Hundreds of rules. Dozens of YAML files. And you still miss things — because you can't write rules for problems you haven't imagined yet.

The Solution

Scherlok takes the opposite approach: learn first, then detect.

scherlok connect postgres://user:pass@host/db   # connect once
scherlok investigate                              # learn your data
scherlok watch                                    # detect anomalies

Three commands. Five minutes. Done.

What It Catches

Anomaly	What Happened	Severity
Volume drop	Row count dropped 40% overnight	CRITICAL
Volume spike	3x more rows than normal	WARNING
Freshness alert	Table hasn't updated in 12h (normally every 2h)	CRITICAL
Schema drift	Column removed or type changed	CRITICAL
NULL surge	NULL rate jumped from 2% to 45%	WARNING
Distribution shift	Column mean shifted 5+ standard deviations	WARNING
Cardinality explosion	Status column went from 5 values to 500	CRITICAL

Every anomaly is auto-scored: INFO, WARNING, or CRITICAL. No thresholds to configure.

Works with dbt

Already running dbt? Scherlok complements dbt test with automatic anomaly detection — no rules to write.

pip install scherlok[dbt]

# After `dbt run`, point Scherlok at your project
scherlok dbt --project-dir ./my_dbt_project

Scherlok reads target/manifest.json, discovers every materialized model (table, incremental, view), auto-resolves the connection from your profiles.yml, and profiles each model:

Investigating 4 dbt models in ./my_dbt_project (postgres)
  ✓ stg_customers                  (12,345 rows)
  ✓ stg_orders                     (98,765 rows)
  ✗ fct_orders                     CRITICAL: Row count dropped 42% (98,765 → 57,283)
  ✓ dim_customers_inc              (12,300 rows)

Summary: 4 profiled, 1 anomalies (1 critical, 0 warning)

Use it as a CI gate after dbt run:

- run: dbt run --target prod
- run: scherlok dbt --project-dir . --target prod --fail-on critical

Or collapse both steps into one with the wrapper:

- run: scherlok dbt-run-and-watch --project-dir . --target prod --fail-on critical

Supported adapters: postgres, bigquery, snowflake, mysql, duckdb. For others, pass --connection-string explicitly.

📖 Full docs: dbt integration guide →

HTML dashboard

scherlok dashboard --out report.html

One self-contained HTML file (~28 KB): KPIs, per-table incidents grouped with first-seen timestamps, +/−/~ schema-drift diff, sparklines, and full anomaly history. Auto dark/light theme via prefers-color-scheme.

📖 Full docs: dashboard guide →

Use it from an AI agent (MCP)

Let Claude Code / Claude Desktop run data-quality checks directly:

pip install scherlok   # scherlok-mcp ships built-in since v0.7.0

{
  "mcpServers": {
    "scherlok": {
      "command": "scherlok-mcp",
      "env": { "SCHERLOK_CONNECTION": "postgresql://user:pass@host/db" }
    }
  }
}

The agent gets list_tables, investigate, watch, status, history, and check as tools. Credentials are resolved server-side (never passed by the model), every operation is read-only on the warehouse, and there's no arbitrary-SQL tool.

📖 Full docs: MCP server guide →

How It Works

1. investigate — Learn the patterns

$ scherlok investigate

  Profiling 12 tables...
  ✓ users         — 45,231 rows, 8 columns
  ✓ orders        — 1,203,847 rows, 15 columns
  ✓ products      — 892 rows, 12 columns
  ...
  Done. Profiles saved.

Scherlok profiles every table: row counts, column types, NULL rates, value distributions, freshness cadence, cardinality. Stores everything locally in SQLite.

2. watch — Detect anomalies

$ scherlok watch

  Checking 12 tables against learned profiles...

  🔴 CRITICAL  orders    volume_drop     Row count dropped 52% (1,203,847 → 578,412)
  🟡 WARNING   users     null_increase   Column "email": NULL rate 2.1% → 18.7%
  🔵 INFO      products  distribution    Column "price": mean shifted 3.2σ

  3 anomalies detected. Exit code: 1

3. Alert — Slack, CI/CD, or both

# Slack
scherlok watch --webhook https://hooks.slack.com/services/...

# Discord
scherlok watch --webhook https://discord.com/api/webhooks/...

# Microsoft Teams
scherlok watch --webhook https://outlook.office.com/webhook/...

# Any endpoint (generic JSON payload)
scherlok watch --webhook https://my-api.com/alerts

# CI/CD gate (fails pipeline on CRITICAL)
scherlok watch --exit-code --fail-on critical

Auto-detects Slack, Discord, and Teams from the URL and formats the payload accordingly. Any other URL receives a generic JSON payload.

CI/CD Integration

Use Scherlok as a data quality gate. The ci command does it in one line:

# GitHub Actions
- name: Data quality check
  run: |
    pip install scherlok
    scherlok config --store s3://my-bucket/scherlok/profiles.db
    scherlok ci ${{ secrets.DATABASE_URL }} \
      --webhook ${{ secrets.SLACK_WEBHOOK }} \
      --fail-on critical

If Scherlok detects a critical anomaly, the pipeline fails. Bad data never reaches production.

Email alerts

export SCHERLOK_SMTP_HOST=smtp.gmail.com
export SCHERLOK_SMTP_USER=alerts@company.com
export SCHERLOK_SMTP_PASSWORD=app-specific-password

scherlok watch --email team@company.com --email cto@company.com

Connectors

# PostgreSQL
scherlok connect postgres://user:pass@host:5432/db

# BigQuery — see src/scherlok/connectors/bigquery.md for auth, billing, CI patterns
pip install scherlok[bigquery]
scherlok connect bigquery://project-id/dataset-name

# Snowflake
pip install scherlok[snowflake]
export SNOWFLAKE_USER=...
export SNOWFLAKE_PASSWORD=...
export SNOWFLAKE_WAREHOUSE=...
scherlok connect snowflake://account/database/schema

# MySQL
pip install scherlok[mysql]
scherlok connect mysql://user:pass@host:3306/dbname

# DuckDB
pip install scherlok[duckdb]
scherlok connect duckdb:///path/to/file.db

Database	Status
PostgreSQL	Available
BigQuery	Available
Snowflake	Available
MySQL	Available
DuckDB	Available

Remote Storage

Share profiles across CI runs and team members:

# AWS S3
scherlok config --store s3://my-bucket/scherlok/profiles.db

# Google Cloud Storage
scherlok config --store gs://my-bucket/scherlok/profiles.db

# Azure Blob Storage
scherlok config --store az://my-container/scherlok/profiles.db

Why Not [Other Tool]?

	Great Expectations	Soda	Monte Carlo	Scherlok
Setup time	Hours	30 min	Weeks	5 minutes
Config required	Hundreds of rules	YAML checks	Dashboard setup	None
Anomaly detection	Manual thresholds	Paid feature	Yes	Yes, free
Self-hosted	Yes	Limited	No (SaaS)	Yes
CI/CD gate	Yes	Yes	No	Yes
Price	Free	Freemium	$50-200K/yr	Free, forever

CLI Reference

scherlok connect <url>          Connect to a database
scherlok investigate            Profile all tables (learn patterns)
scherlok watch [-w <url>] [-e <email>]  Detect anomalies and alert
scherlok ci <url> [opts]        All-in-one CI/CD command (connect + watch + exit code)
scherlok status                 Quick health dashboard
scherlok report                 Detailed profile summary
scherlok history [--days N]     Timeline of past anomalies
scherlok config --store <url>   Set remote storage
scherlok version                Show version

Install

pip install scherlok

# With BigQuery support
pip install scherlok[bigquery]

Requires Python 3.10+.

Run via Docker

A pre-built image with every warehouse extra (dbt, bigquery, snowflake) is published to GitHub Container Registry on every release tag:

docker run --rm ghcr.io/rbmuller/scherlok:latest version

Mount your project directory and inject connection details the same way your CI does it; the entrypoint is the scherlok CLI:

docker run --rm \
  -v "$PWD:/work" -w /work \
  -e SCHERLOK_CONNECTION=postgres://... \
  ghcr.io/rbmuller/scherlok:latest watch

The image is built from python:3.12-slim and runs unprivileged (USER scherlok).

Contributing

Contributions welcome! See CONTRIBUTING.md.

We're especially looking for:

• New database connectors (Snowflake, MySQL, DuckDB)
• Anomaly detection improvements
• Documentation and examples

License

MIT — Developed by Robson Bayer Müller