Data Quality Tools: The Best Data Quality Software and Monitoring Tools for 2026
Twelve real tools, what each one actually checks, where it stops, and the published price when there is one. No invented figures, no vendor scores.
Read-only connection. Datatrail never moves or mutates your data.
In short
Data quality tools verify that the data in your warehouse is accurate, complete, fresh, and consistent, and alert someone when it is not. The leading options in 2026 are Datatrail, Great Expectations, Soda, dbt tests, Elementary, Monte Carlo, Anomalo, Bigeye, Collibra Data Quality, Informatica Data Quality, Ataccama ONE, and the native quality features in Snowflake and Databricks. They divide cleanly into three groups: test frameworks where you write the rules, observability platforms that learn normal behavior and flag deviations automatically, and enterprise governance suites where quality is one module of a compliance program. Most teams need one from the first group and one from the second.
Last updated July 2026
Side by side
Data quality software compared
| Tool | Best for | Approach | Rules written in | Pricing model |
|---|---|---|---|---|
| Datatrail | Teams that want automatic monitoring plus column-level lineage | Monitoring + lineage | None required | Public, self-serve |
| Great Expectations | Python teams writing precise custom assertions | Test framework | Python | Open source, paid cloud |
| Soda | Teams that want declarative YAML checks with published prices | Test framework + cloud | SodaCL (YAML) | Free, then $750/mo |
| dbt tests | Anyone already modeling in dbt who wants a free baseline | Test framework | YAML + SQL | Included with dbt |
| Elementary | dbt-only teams wanting free observability on top of tests | Observability (dbt-native) | YAML | Open source, paid cloud |
| Monte Carlo | Large enterprises wanting broad automated anomaly detection | Observability | None required | Custom quote, sales-led |
| Anomalo | Warehouse-native teams wanting unsupervised anomaly detection | Observability (AI-native) | None required | Custom quote, sales-led |
| Bigeye | Teams that need explicit, enforceable data SLAs | Observability | None required | Custom quote, sales-led |
| Collibra Data Quality | Regulated enterprises running a formal governance program | Governance suite | None required | Custom quote, sales-led |
| Informatica Data Quality | Enterprises standardized on Informatica or Salesforce | Governance suite + MDM | Low-code | Custom quote, sales-led |
| Ataccama ONE | Organizations that need quality, governance, and MDM together | Governance suite + MDM | Low-code | Custom quote, sales-led |
| Warehouse-native quality | Getting a quality baseline with no new vendor | Platform feature | SQL / console | Metered by platform |
Capabilities and pricing models reflect each vendor's public positioning as of July 2026 and are provided in good faith. Only figures a vendor publishes itself appear here. Vendors change quickly, so confirm current details directly before you buy.
The three families
Data quality tools split into three very different products
Test frameworks
You write the rule, the tool enforces it. dbt tests, Great Expectations, and Soda live here. They are precise, cheap, and completely under your control, which is exactly why teams start with them. The trade is that coverage stops where somebody stopped writing tests, and schemas move faster than test suites do.
Observability platforms
The tool learns how a table normally behaves and tells you when it does not. Datatrail, Monte Carlo, Anomalo, Bigeye, Metaplane, and Elementary Cloud sit here. Coverage is automatic across every table, including the ones nobody documented. The trade is that a statistical alert needs context before anyone can act on it.
Governance suites
Quality is one module inside a platform built for policy, stewardship, glossary, and audit. Collibra, Informatica, and Ataccama own this space, and for a regulated enterprise they are the correct answer. The trade is scope and time: these are programs with rollouts, not tools you turn on this afternoon.
The mistake that costs the most money is buying across families by accident. A compliance officer asks for data quality, someone shortlists three observability platforms, and six months later nobody can produce the audit evidence the request was actually about. It also runs the other way: a data engineering team inherits a governance suite and discovers that nothing in it answers why last night's revenue number moved.
The useful diagnostic is to ask who raised the problem and what they will do with the answer. An engineer about to change a schema wants downstream impact analysis. An analyst who was embarrassed by a broken dashboard wants freshness monitoring. A risk function wants attestable controls. Those are three different purchases.
The tools
What each data quality tool is actually good at
Datatrail
Public, self-serveDatatrail connects to your warehouse with a read-only role, parses query history and your dbt graph into column-level lineage, and derives freshness, volume, and schema-drift monitoring from that graph without you writing a single test. The point of the lineage is triage: every alert arrives with the models, exposures, and dashboards that read the affected column, listed by name. It is deliberately not a governance suite, and there is no stewardship workflow or business glossary.
Great Expectations
Open source, paid cloudGX Core is the most widely used open-source data quality framework in Python, and it is free under Apache 2.0. You define Expectations, group them into Suites, and run them through Checkpoints in your pipeline. That gets you assertions no product ships out of the box, like a revenue column reconciling to a ledger. The ceiling is structural: coverage equals the tests somebody wrote, and GX builds no lineage graph, so a failure tells you a column is wrong but not what it feeds.
Soda
Free, then $750/moSoda replaces Python assertions with SodaCL, a readable YAML check language, so analysts and analytics engineers can write and review quality rules without owning a Python codebase. Soda Cloud adds anomaly detection, alerting, and collaboration on top. It is one of the very few vendors in this category that publishes real numbers: its pricing page lists a free tier at $0 per month and a Team plan at $750 per month, with Enterprise on a custom quote.
dbt tests
Included with dbtIf you run dbt, you already have data quality tooling. Generic tests cover not null, unique, accepted values, and relationships in a few lines of YAML, and singular tests let you assert anything you can write as SQL. This should be the first thing any team turns on because it costs nothing and catches the boring failures. What it will not do is watch tables outside your dbt project or notice a problem nobody thought to assert.
Elementary
Open source, paid cloudElementary installs as a package inside your dbt project, stores test results and run artifacts in your own warehouse, generates a report, and alerts to Slack or Teams. The open-source version is genuinely free and genuinely useful, and it turns scattered dbt test output into something a team will actually read. The boundary is the dbt project: raw landing tables, ad hoc queries, and jobs outside dbt are invisible to it.
Monte Carlo
Custom quote, sales-ledMonte Carlo coined the phrase data downtime and remains the most complete enterprise data observability platform: machine-learned monitors across freshness, volume, schema, and distribution, with lineage layered in for triage, spanning warehouses, lakes, ETL, and BI. It is bought rather than tried. Expect a sales cycle, a custom quote, and a rollout plan. That overhead is fine for a large data org with a reliability budget and heavy for a five-person team.
Anomalo
Custom quote, sales-ledAnomalo is the strongest option in the category for finding value-level problems nobody thought to check: it learns what a table normally looks like and flags unsupervised anomalies in the data itself, not just its shape. It runs its checks as compute inside your warehouse rather than pulling data out. Lineage is table-level and refreshed daily rather than native column-level, and Anomalo does not publish pricing.
Bigeye
Custom quote, sales-ledBigeye leans into deep, configurable monitoring: metric-level thresholds, autothresholds that adapt to a table history, and explicit data SLAs you can hold an owning team to. If your mandate is to prove that a dataset met an agreed standard last quarter, that framing is the reason to shortlist it. Lineage exists and supports triage, but the center of gravity is the monitor rather than the graph, and pricing is quote-based.
Collibra Data Quality
Custom quote, sales-ledCollibra bought OwlDQ and folded it into its governance platform, so data quality rules sit next to policies, stewardship workflows, and a business glossary, across a connector estate that includes legacy systems most modern tools ignore. When a compliance function is driving the purchase and someone has to attest to a control, that integration is the whole point. When an engineer just wants to know why a number moved, it is a great deal of machinery.
Informatica Data Quality
Custom quote, sales-ledInformatica Data Quality is the incumbent for profiling, cleansing, standardization, and matching at enterprise scale, usually bought alongside its MDM and integration stack. Salesforce completed its acquisition of Informatica on 18 November 2025 in a deal valued at roughly $8 billion, folding the catalog, governance, quality, and MDM services into its own data platform. That is the main thing to weigh now: where the roadmap points, and how much of it assumes Salesforce.
Ataccama ONE
Custom quote, sales-ledAtaccama ONE unifies data quality, cataloging, governance, and master data management in a single platform, with strong rule-based profiling and cleansing and a credible AI layer for rule suggestion. It is a serious answer for a large or regulated organization that wants one vendor across all four jobs. Ataccama does not publish pricing, and third-party figures floating around the web are not from Ataccama, so get a quote rather than trusting a listing.
Warehouse-native quality
Metered by platformSnowflake data metric functions and Databricks data quality monitoring both give you checks without buying anything new. Snowflake requires Enterprise Edition, bills the serverless compute under a Data Quality Monitoring line, and caps you at 50,000 DMF associations per account. Databricks data quality monitoring, formerly Lakehouse Monitoring, needs a Unity Catalog enabled workspace plus Databricks SQL access and runs on serverless job compute. Both stop at their own platform boundary.
Build versus buy
What the free data quality tools really cost
Every honest version of this comparison has to deal with the fact that four of the twelve options cost nothing to license. dbt tests ship with dbt. GX Core is Apache 2.0. Soda has a free tier. Elementary open source is free. That is a real answer for a real set of teams, and anyone telling you otherwise is selling something.
The cost shows up somewhere else. A test suite is code you own: it lives in your repo, runs in your orchestrator, and rots the moment a model changes without a matching test change. Teams routinely find that the person who wrote the suite has left and nobody has touched it in eight months, which means the checks are still green and no longer mean anything. Budget an ongoing slice of an engineer, not a one-time setup.
The second cost is the gap. Tests protect the columns somebody named. Most incidents happen in the space nobody named: the raw landing table an analyst quietly started querying, the field a vendor renamed upstream, the job that stopped running and left yesterday's numbers in place. No amount of test discipline covers that, because the whole failure mode is not having thought of it.
The platform-native options have a different shape of cost. Snowflake data metric functions require Enterprise Edition, bill serverless compute under a Data Quality Monitoring line on your invoice, and cap out at 50,000 DMF associations per account, with no support for hybrid tables or streams. Databricks data quality monitoring, previously called Lakehouse Monitoring, needs a Unity Catalog enabled workspace and Databricks SQL access, and runs on serverless job compute billed as its own product line. Both are good and both stop at the edge of their own platform, which is a problem the moment your data leaves it for a BI tool.
The pragmatic setup for most teams is layered: dbt tests for the invariants you can articulate, an automatic monitoring layer for the several hundred tables nobody will ever write tests for, and column-level lineage underneath both so that when something fires you know in seconds whether it matters.
How to choose
Four questions that decide which data quality tool you need
Is coverage automatic?
Ask whether a brand new table is monitored on day one or only after somebody writes a check for it. That single answer separates test frameworks from observability platforms, and it predicts which incidents you will still be surprised by a year from now.
Where does it stop?
dbt-native tools stop at the dbt project. Warehouse-native features stop at the platform. Ask specifically whether coverage reaches the raw landing tables upstream and the dashboards downstream, because that is where a data quality failure is actually noticed.
Do alerts carry impact?
An alert that says a column looks wrong creates work. An alert that lists the eleven models and two executive dashboards reading that column creates a decision. Without lineage, every alert costs the same investigation, which is how teams end up muting the channel.
How is it bought?
Most vendors here will not tell you a price without a call, and that is a schedule decision as much as a budget one. A sales-led platform costs a quarter of evaluation and rollout. Soda and Datatrail publish numbers you can read today.
Where we fit
When Datatrail is the right pick, and when it is not
Datatrail comes at data quality from the lineage side. It connects to Snowflake, BigQuery, Redshift, Databricks, or Postgres with a read-only role, parses query history alongside your dbt manifest, and resolves the dependency graph down to individual fields. From that graph it derives monitoring you never had to write: freshness expectations based on each table's own load pattern, volume anomalies, and schema change alerts when a column is dropped, renamed, or retyped. Every alert is ranked by what it breaks, and before you ship a change you can ask the same question in reverse and see the blast radius first.
We are the wrong tool in two clear cases. If you need to express a precise business invariant, the kind where revenue must reconcile to a ledger within a cent, write it as a Great Expectations assertion or a dbt test. Statistical monitoring will never replace a rule you can state exactly. And if you are buying a governance program with stewardship workflows, a glossary, and audit attestation, look at Collibra or Informatica instead, because we do none of that.
Where we are hard to beat is the case most data teams are actually in: a warehouse with far more in it than dbt models, a handful of dashboards executives genuinely read, an on-call rotation that is tired of alerts without context, and no appetite for a six-month rollout. If that is you, see how the graph gets built in column-level lineage, compare the neighbors in our data lineage tools guide, or read the head-to-head pages for Monte Carlo, Anomalo, and Bigeye.
Questions people ask
Data quality tools, answered
What are data quality tools?
Data quality tools check whether the data in your warehouse is correct, complete, fresh, and consistent, then tell someone when it is not. They fall into two families: test frameworks where you write the rules yourself, like Great Expectations, Soda, and dbt tests, and observability platforms that learn normal behavior and flag deviations automatically, like Monte Carlo, Anomalo, Bigeye, and Datatrail.
What is the best data quality tool?
There is no single best data quality tool, because the category answers three different questions. If you need to assert specific business rules in code, Great Expectations or Soda. If you need automatic coverage across hundreds of tables nobody will write tests for, an observability tool like Datatrail, Monte Carlo, or Anomalo. If a compliance program is driving the purchase, Collibra, Informatica, or Ataccama.
What is the difference between data quality and data observability?
Data quality is the outcome you want: data that is accurate, complete, and timely. Data observability is one method of getting there, based on continuously watching metadata such as freshness, volume, schema, and distribution to detect when something changed. Test-based data quality confirms rules you already knew to write. Observability finds the failures you never anticipated. Mature teams run both.
Are there free data quality tools?
Yes, and you should start with them. dbt tests are included with dbt, Great Expectations GX Core is Apache 2.0 licensed, Soda offers a free tier, and Elementary open source gives dbt teams real observability at no license cost. Snowflake and Databricks both include native quality monitoring, billed as metered compute. The real cost of the free options is the engineering time to write and maintain them.
How much do data quality tools cost?
Most enterprise vendors, including Monte Carlo, Anomalo, Bigeye, Collibra, Informatica, and Ataccama, do not publish pricing and quote per deployment based on connected sources, monitored tables, and seats. Soda is a rare exception and lists a free tier and a Team plan at $750 per month on its pricing page. Datatrail also publishes a self-serve price you can read without a sales call.
Do I need data lineage for data quality?
You do not need lineage to detect a data quality problem, but you need it to act on one quickly. Without lineage, an alert says a column looks wrong. With column-level lineage, the same alert says which downstream models, exposures, and dashboards read that column, so you know whether to page someone at 2am or fix it on Monday. Lineage is what turns alerts into triage.
What should I look for in a data quality tool?
Four things decide it. Whether coverage is automatic or limited to tests you write. Whether the tool reaches beyond one platform or dbt project. Whether alerts carry downstream impact so people can prioritize them. And whether the buying process fits your team, because a sales-led platform costs a quarter of evaluation while a self-serve tool costs an afternoon.
Quality monitoring that knows what breaks
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