The best data validation tools in 2026

Great Expectations, Soda, dbt tests, Monte Carlo, Elementary, Catalyst and plain SQL — what each is good at, where each falls short, and how to choose.

2026-09-10 · 12 min read

There is no best data validation tool, only a best fit, and the fit is decided by three things: who writes the rules, when the checks need to run, and how much engineering time you will spend on plumbing. If your team is comfortable in Python and wants total control, Great Expectations is still the deepest option. If everything you care about is a dbt model, dbt tests plus Elementary may be all you ever need. If the people who know the business rules do not write code, or the tables that break are not the ones dbt owns, you want a hosted monitoring product — Soda, Monte Carlo or Catalyst, at very different price points and levels of maturity.

We build Catalyst, so read this with that in mind. What follows is what we would tell someone who asked in person, including talking them out of Catalyst when it is the wrong answer.

Two distinctions that decide most of it

Validation versus observability. Validation checks data against rules you wrote: this column is never null, this identifier is unique, this status is one of five values. Observability detects that something changed without you declaring what "correct" is — volume dropped, a schema shifted, a distribution moved. Most teams need far more of the first than vendors selling the second like to admit, because most real incidents are boring: a load that did not run, a key that duplicated, a field that went empty.

Library versus product. A library gives you a rule engine and leaves scheduling, storage, alerting and access control to you. A product gives you all of that and less control. Which is right depends on whether you have an engineer who wants that plumbing.

Great Expectations

What it is. The best-known open-source validation framework, Python-native. You define expectations — an extensive catalogue, from expect_column_values_to_not_be_null through to distributional checks — group them into suites, and run them against pandas dataframes, Spark, or SQL databases via SQLAlchemy.

Strengths. The widest expectation catalogue here, reaching places rule-based tools usually do not: quantile ranges, KL divergence, column pair relationships. It runs anywhere Python runs, so it can validate data before it reaches a warehouse — in an ingestion job, a notebook, a file — which is a genuinely different capability from anything that only speaks SQL against tables. Mature, widely used, well documented, free.

Limitations. It is a library, so the operational surface is yours: scheduling, result storage, alert routing, access control. Configuration is extensive, and teams frequently report setup taking longer than expected. The API has changed significantly between major versions, so tutorials and internal code age badly. Non-engineers cannot realistically maintain expectations.

Best for. Python-first teams with engineering capacity, especially where validation must happen inside pipelines rather than on warehouse tables. See also Catalyst vs Great Expectations.

Soda

What it is. A data quality platform built around SodaCL, a readable YAML-style check language. Two forms: Soda Core, the open-source scanner you run yourself, and a hosted product adding scheduling, dashboards and incident tracking.

Strengths. SodaCL hits a genuinely good balance — more readable than Python, more expressive than dbt's generic tests, close enough to plain English that an analyst can review a check they would not have written. Warehouse coverage is broad, and the open-core model is honest: start with Soda Core free, run it in CI, move to the hosted product when you want the interface, without rewriting your checks.

Limitations. The free tier is a scanner, not a monitoring product — history, alerting and a UI for the business sit in the paid tier. Checks are still files someone maintains in a repository, so the "non-engineers write rules" story is partial. Hosted pricing is not publicly itemised in a way that lets you predict it.

Best for. Teams that want a readable check language and are happy running a scanner, with an upgrade path. See also Catalyst vs Soda.

dbt tests

What it is. Assertions declared in your dbt project's schema.yml, compiled into SQL that must return zero rows. Four generic tests built in — unique, not_null, accepted_values, relationships — extended considerably by dbt_utils and dbt-expectations, plus singular tests for arbitrary SQL.

Strengths. If you already run dbt, this is free and immediate. Tests live next to the model they describe, reviewed in the same pull request as the transformation, which is the correct place for them. They run in CI and block bad models from being published. For catching a join that fanned out or a key that stopped being unique, nothing is better positioned.

Limitations. They run only when dbt runs, so detection latency equals build cadence — and if the build is skipped, so are the tests. They cover the dbt project, excluding raw sources loaded by other tools and anything uploaded by hand. No result history or interface without another tool, alerting delegated to your orchestrator, authoring requires repository access.

Best for. Any team already using dbt, as build-time assertions. Not sufficient alone once anything outside the dbt project matters. More in Catalyst vs dbt tests.

Monte Carlo

What it is. The best-known data observability platform. Rather than starting from rules you write, it profiles your tables and learns what normal looks like — volume, freshness, schema, distributions — then alerts on deviations, with lineage showing what is downstream.

Strengths. The coverage-without-configuration story is real: point it at a warehouse and it finds problems in tables nobody thought to write rules for, exactly the class of incident rule-based tools miss. Field-level lineage and incident management are strong, and the impact analysis — this table broke, here are the twelve dashboards affected and who owns them — is hard to replicate. The mature choice for large estates.

Limitations. It is priced for enterprises. We will not quote a list price we cannot verify, but it is not a departmental purchase and it comes with a sales process. Anomaly detection produces false positives during any legitimate change — a promotion, a new market, a backfill — and tuning that is ongoing work. It complements explicit rules rather than replacing them: no model infers that a refund must never exceed the original order value.

Best for. Large organisations with hundreds or thousands of tables, a platform team, and enterprise budget. Substantial overkill for a team of five with twenty important tables.

Elementary

What it is. Observability built specifically for dbt. A dbt package captures test results and run metadata into your warehouse; the open-source edition adds a generated report, Slack alerting and anomaly-detection tests, and the cloud offering layers a hosted UI and managed operation on top.

Strengths. For a dbt shop this is the highest-value-per-hour option on the list. It turns dbt test results — which otherwise live in JSON artifacts nobody reads — into history, trends and Slack alerts, with a package install rather than a new platform. The open-source core is real, not a trial. Anomaly monitors add volume and freshness detection that generic dbt tests do not cover.

Limitations. dbt-native by design, which is its strength and its ceiling: the world it sees is the world dbt knows about. Tables loaded outside your dbt project, or a database nobody has modelled, are out of scope. Its metadata lands in your warehouse, so you own that storage. And authorship stays a repository activity.

Best for. Teams all-in on dbt wanting observability over their existing tests without a separate platform.

Catalyst

What it is. Our product: a hosted, EU-based data quality monitoring application built on the Open Data Contract Standard. Connect a database read-only, import a table's schema, review the rules it proposes, and it runs them on a schedule with pass/warn/fail history and example failing rows to drill into. Rules are stored as ODCS YAML contracts; the visual builder and the YAML are the same document.

Strengths. No code is required to be useful — the person who knows a status field only ever takes five values can write that rule in a browser, which in our experience is the most common blocker to coverage. It monitors any table in a connected database, not only what a transformation framework owns, plus CSV, JSON and Excel uploads. Connectors cover PostgreSQL, MySQL, SQL Server, BigQuery, Redshift and Microsoft Fabric; rules cover not-null, uniqueness, ranges, patterns, allowed values, freshness, referential integrity and custom SQL. The checks run as SQL in your warehouse, and Catalyst stores results, violation counts and up to five example failing rows per check, never the dataset itself. RBAC, SSO and an audit log are included, and pricing is public: free Starter tier, Team at €29 per user per month, Enterprise on request — see pricing.

Limitations, honestly. It is a young product without the operational track record of Great Expectations or Monte Carlo. There are no Snowflake or Databricks connectors at the time of writing, which rules it out for a large share of the market outright. It is not open source, so you cannot self-host it or read the engine. It does rule-based validation, not ML-driven anomaly detection, so it will not surprise you with a problem you never described. And it has no lineage graph: it will tell you a table is wrong, not enumerate every dashboard downstream of it.

Best for. Small and mid-sized teams where data owners are not engineers, where important tables include sources outside a dbt project, and where EU hosting matters. A poor fit on Snowflake or Databricks today, or if you want unsupervised anomaly detection.

Plain SQL and your orchestrator

What it is. The baseline everyone should price against: a folder of .sql files counting nulls, duplicates and stale rows, run as tasks in Airflow, Dagster or cron, failing when a count is non-zero.

Strengths. Free, no new vendor, no new concepts, works on every database you have, runs on whatever schedule you already orchestrate. For a handful of critical checks this is the correct amount of tooling, and a team that does it well is in better shape than one that bought a platform and configured nothing.

Limitations. It does not scale past a few dozen checks. Every check is bespoke, so there is no coverage view, no consistent severity model, no history unless you build a results table, and no way for anyone outside engineering to see or change anything. The maintenance burden stays invisible until the person who wrote it leaves. And nobody ever gets around to the freshness checks.

Best for. Teams with fewer than twenty checks, or as a deliberate first step to learn which checks matter before buying anything.

Side by side

How to choose

Work through these in order; the first that applies usually decides it.

By stack. On Snowflake or Databricks, your shortlist at the time of writing is Great Expectations, Soda, Monte Carlo, or dbt plus Elementary. If everything you care about is already a dbt model and the build runs often enough, start with dbt tests plus Elementary and stop there. If your important tables live in Postgres, MySQL, SQL Server, BigQuery, Redshift or Fabric and include things dbt never touches, a scheduled monitor covers the gap.

By who writes the rules. The question most often skipped, and the one that decides whether a tool is still used a year later. If the business rules live with people who do not open a repository, pick something with a UI they can log into. Otherwise a code-first tool is fine and probably better.

By team size. Under five: plain SQL or dbt tests, plus one hosted monitor if sources outside dbt matter. Five to fifty: a hosted validation product, keeping dbt tests for build-time assertions. Fifty and up with a platform team and hundreds of tables: observability with lineage starts to justify itself.

By budget. Zero: Great Expectations, Soda Core, dbt tests, Elementary's open-source core, or SQL. A departmental line item: Soda, Elementary Cloud or Catalyst. Enterprise budget across a large estate: Monte Carlo.

By what breaks. If your incidents are transformation bugs, invest in build-time tests. If they are late or missing loads — which, in most teams, they are — scheduled freshness monitoring is the highest-return thing you can do, and it is cheap in every tool here.

Sequencing matters more than the choice: cover ten important datasets with five obvious rules each before evaluating anything more sophisticated. The method is in our guide to validating your data.

Frequently asked questions

What is the difference between data validation and data observability?

Validation checks data against rules you declared: this column is never null, this table is refreshed daily, this status is one of five values. Observability learns a baseline from your data's history and alerts when something deviates — volume, freshness, schema or distribution — without you specifying what correct looks like. Validation catches the failures you can anticipate and is cheap to run; observability catches unknown unknowns and costs more, in money and in false positives. Mature teams use both, but almost everyone should get explicit rules on their critical datasets first.

Are there free data validation tools?

Yes, several good ones. Great Expectations is fully open source, Soda Core is a free open-source scanner, dbt tests are included with dbt, and Elementary has an open-source package and report. Catalyst has a free Starter tier. The honest caveat is that free usually means "free library, your operations": you still supply scheduling, result storage, alert routing and access control, and that engineering time is the real cost.

Do I need a data validation tool if I already use dbt tests?

Only if something outside your dbt project matters. dbt tests are excellent at build-time assertions on the models dbt manages, and if every dataset people make decisions with is a dbt model and your build runs frequently, you may be well covered. The gaps appear with raw sources loaded by other tools, databases nobody has modelled, files uploaded by hand, and the absence of result history or an interface for non-engineers.

Which data validation tool is best for a small team?

Setup and maintenance time usually decides it, not features. If you run dbt, dbt tests plus Elementary's open-source report gets you a long way for the cost of a package install. If the tables that matter are not dbt models, or the rules live with people who do not write code, a hosted monitor with a free tier — Catalyst, or Soda Core if you would rather run the scanner yourself — avoids building scheduling and result history from scratch.

What should I check first when setting up data quality monitoring?

Freshness, on your most-used datasets. The most common data incident is not wrong data but absent data — a load that silently did not run, leaving yesterday's numbers looking entirely valid. After that, add not-null on the columns your reports group by, uniqueness on your primary keys, and allowed values on any status field owned by another system. Those four rule types cover a disproportionate share of real incidents and take an afternoon to set up.