SaaS or self-hosted
Datafold
Pre-merge data diffing and column-level lineage — the tool that shifts data quality left into the pull request.
Data Stack Index
Updated 2026-04-25
Data quality
& testing
Tools for catching bad data — before it hits a dashboard, an ML model, or an executive.
Data quality tooling splits cleanly along two fault lines. The first is where the tool lives: inside the dbt codebase (Elementary, dbt-expectations, Great Expectations), or outside it watching the warehouse (Monte Carlo, Bigeye, Metaplane, Anomalo). The second is how it decides what’s wrong: explicit assertions you write, or ML models that learn normal and flag deviations.
These aren’t mutually exclusive — the mature teams run both paradigms — but picking the wrong primary tool for your context wastes a quarter and a budget. A team with all their logic in dbt and no ingestion-layer problems doesn’t need Monte Carlo’s warehouse-side surveillance. A team with Fivetran-plus-Airbyte-plus-custom-Python loading data that dbt never sees will be blind to most of their incidents with Elementary alone.
This page is organized to make that choice legible.
§01
Questions this page answers
01
Should I use dbt-native testing, warehouse-native monitoring, or both?
02
Do I need ML anomaly detection, or are assertions enough for my data?
03
Can I get what I need from an open-source tool, or is managed worth the money?
04
Which tools actually prevent bad data from propagating — versus only alerting?
05
How do teams typically move between these tools as they scale?
06
What does "data contracts support" actually mean vendor-by-vendor?
§02
Primary tools in this cluster
open source SaaS or self-hosted
Elementary
The dbt-native observability layer — tests, anomaly detection, and lineage that live inside your dbt project.
SaaS
Monte Carlo
Warehouse-side data observability for teams whose problems are upstream of dbt — ingestion, streaming, and across the full pipeline.
§03
Capability matrix
| Tool | ML anomaly detection | dbt-native | Pre-merge diffing | Circuit breaker | Monitors at |
|---|---|---|---|---|---|
| Datafold | — | ✓ | ✓ | ✓ | table, column, dbt model |
| Elementary | ✓ | ✓ | — | — | dbt model, table, column |
| Monte Carlo | ✓ | — | — | ✓ | table, column, dbt model, pipeline task, bi dashboard, ml feature |
§04
By specific capability
A note on coverage
Every tool listed here was verified by hand against vendor documentation and, where possible, hands-on trial. Capability claims are independent of vendor marketing language. When a capability is partial or caveated, the individual tool page explains how.
Notice something wrong? Send a correction. We log every edit and surface the last-verified date on every tool page.