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 its logic in dbt and no ingestion-layer problems doesn’t need Monte Carlo’s warehouse-side surveillance; a team loading via Fivetran, Airbyte, and custom Python will be blind to most incidents with Elementary alone. 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.
The questions a buyer brings.
- 01Should I use dbt-native testing, warehouse-native monitoring, or both?
- Both, eventually. dbt-native tools (Elementary, dbt-expectations, Great Expectations) test inside the dbt run — close to your models but blind to anything dbt doesn't touch. Warehouse-native monitors (Monte Carlo, Bigeye, Anomalo) watch the warehouse itself and catch ingestion-layer breakage upstream of dbt. Teams with all their logic in dbt can start dbt-native; teams loading data dbt never sees need warehouse-side coverage too.
- 02Do I need ML anomaly detection, or are assertions enough for my data?
- Assertions catch the failures you can name — known invariants, business rules, referential integrity — cheaply and deterministically. ML anomaly detection (Monte Carlo, Anomalo, Bigeye) learns each table's normal and flags deviations you didn't think to test, at the cost of a 14–30 day training window and some seasonal false positives. Small, well-understood schemas can live on assertions; large or fast-changing estates benefit from both.
- 03Can I get what I need from an open-source tool, or is managed worth the money?
- Open source (Great Expectations, Soda Core, dbt-expectations, Elementary's core) trades licence cost for operational cost — you run, tune, and upgrade it. Managed platforms trade dollars for time-to-value, ML detection, and incident workflows. A strong small team can run OSS indefinitely; a larger team without the bandwidth usually gets value sooner from a managed tool.
- 04Which tools actually prevent bad data from propagating — versus only alerting?
- Prevention needs a gate, not just an alert. Pre-merge diffing (Datafold) blocks a bad change in the pull request; circuit-breaker support halts a pipeline when an input fails its checks. Monitoring-only tools tell you after the fact. If stopping propagation is the goal, look for runs_pre_merge and circuit_breaker_support in each tool's spec.
- 05How do teams typically move between these tools as they scale?
- The common path: start with dbt tests, add Elementary for run-level visibility, then layer on Datafold (shift-left, pre-merge) or a warehouse-native monitor (Monte Carlo, Bigeye, Anomalo) as ingestion-layer incidents mount. The pattern is additive, not replacement — each tool covers a different stage of the lifecycle.
- 06What does "data contracts support" actually mean vendor-by-vendor?
- It varies by vendor. On the testing side it means enforcement — blocking data that violates a declared schema or semantic contract. On the catalog side it often means declaring and documenting a contract without hard enforcement. Check whether data_contracts_enforcement is real gating or just a registry; the strongest implementations do both.
11 tools, three philosophies.
Acceldata
Acceldata · est. 2018 · Campbell, CA
Enterprise data observability with ML data quality, reconciliation, and a built-in catalog — strong on hybrid and on-prem estates.
Anomalo
Anomalo · est. 2018
GUI-first ML anomaly detection at petabyte scale — pivoting in 2026 around agentic AI and unstructured-data monitoring.
Bigeye
Bigeye · est. 2019
Enterprise data observability with Autometrics ML thresholds — repositioning in 2026 as an AI Trust Platform with runtime governance.
Datafold
Datafold · est. 2020 · San Francisco, CA
Pre-merge data diffing and column-level lineage — the tool that shifts data quality left into the pull request.
dbt-expectations
Metaplane (Datadog) · est. 2020
Open-source dbt package adding 50+ Great Expectations-style assertions as native dbt tests that run in your own warehouse.
Elementary
Elementary Data · est. 2021 · Tel Aviv, Israel
The dbt-native observability layer — tests, anomaly detection, and lineage that live inside your dbt project.
Great Expectations
Great Expectations · est. 2017
Python-native data validation framework — the OSS standard, now in stewardship transition after the May 2026 acquisition.
Metaplane
Metaplane (Datadog) · est. 2019 · Boston, MA
ML-powered, no-code data observability for the dbt and warehouse stack with automatic column-level lineage — now Metaplane by Datadog.
Monte Carlo
Monte Carlo Data · est. 2019 · San Francisco, CA
Warehouse-side data observability for teams whose problems are upstream of dbt — ingestion, streaming, and across the full pipeline.
Sifflet
Sifflet · est. 2021 · Paris, France
EU-built full-stack data observability pairing ML-driven monitoring with an embedded catalog and field-level lineage.
Soda
Soda Data · est. 2019 · Brussels, Belgium
YAML-first data contracts and observability — SodaCL plus Soda Cloud, with anomaly detection and a self-hosted Kubernetes runner.
What each tool ships.
| Tool | 01 dbt-native | 02 ML anomaly | 03 Assertions | 04 Pre-merge | 05 Schema drift | 06 Freshness | 07 Volume | 08 Custom SQL | 09 Circuit-break | 10 Contracts |
|---|---|---|---|---|---|---|---|---|---|---|
| Acceldata | ||||||||||
| Anomalo | ||||||||||
| Bigeye | ||||||||||
| Datafold | ||||||||||
| dbt-expectations | ||||||||||
| Elementary | ||||||||||
| Great Expectations | ||||||||||
| Metaplane | ||||||||||
| Monte Carlo | ||||||||||
| Sifflet | ||||||||||
| Soda |
Scope, alerting channels, and monitoring targets vary by tool — open any tool name above for the full capability spec.
Three trade-offs that matter.
Inside dbt, or outside?
If every transformation lives in dbt, start dbt-native (Elementary). The moment data lands from outside dbt — Fivetran, custom Python, streaming — you need warehouse-native coverage too (Monte Carlo), because dbt-native tools never see ingestion drift, raw-table schema changes, or connector failures.
Assertions, or anomaly detection?
Assertions are tests you write — explicit, cheap, great for known invariants and business rules. ML anomaly detection learns "normal" from history and flags deviation — catches unknown unknowns but needs a 14–30 day training window and produces seasonal false positives. Mature teams run both.
Open-source, or managed?
Break-even is platform maturity, not headcount. A strong 5-person team runs Elementary (OSS) indefinitely; a 30-person team without that bandwidth gets value from managed Monte Carlo on day one — paying dollars to skip the run/tune/upgrade load.
Also strong at quality testing — primarily categorized elsewhere.
These tools earn their primary classification in another cluster (catalog or lineage) but score 2 or 3 of 3 on quality capability — the cluster overlap is real, not aspirational. Worth a look when consolidating two budgets into one.
- Collibra → Primary: catalog discovery · Quality strength 2/3
- DataHub → Primary: catalog discovery · Quality strength 2/3
- OpenMetadata → Primary: catalog discovery · Quality strength 2/3
Drill into one feature.
Compare two side by side.
Every same-cluster pair a buyer realistically shortlists — see all comparisons.
Why these three, and not more.
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.