#1
Deequ
Best forSpark and Scala/Python engineers validating large datasets in batch pipelines
Open-source library from AWS Labs for defining unit tests for data on Apache Spark.
Free plan · #3 overall
8.4/10
Best Open-Source Data Quality Tools in 2026: Ranked with Real Prices
Deequ is the top-ranked open-source data quality tool of the 3 we ranked (8.4/10, free plan), best for spark and Scala/Python engineers validating large datasets in batch pipelines. Elementary is the runner-up (8.4/10).
Open-source data quality tools are free to use and can be run inside your own pipelines, with the checks stored as code next to your transformations. You host and maintain them yourself; some vendors also sell a paid cloud version with a user interface and alerting.
Included if: The core product is released under an open-source licence. We apply this rule to the same data as the full data quality tools ranking, so positions follow the overall score.
Compare Open-Source data quality tools at a glance
| Tool | Score | Free plan | Paid from (annual) | Data profiling | Custom rule checks | ML anomaly detection | Data cleansing / correction | Open source |
|---|---|---|---|---|---|---|---|---|
| Deequ | 8.4/10 | Yes | See vendor | Yes | Yes | Not confirmed | Not confirmed | Yes |
| Elementary | 8.4/10 | Yes | See vendor | Not confirmed | Yes | Yes | Not confirmed | Yes |
| Great Expectations (GX Core) | 7.9/10 | Yes | See vendor | Not confirmed | Yes | Not confirmed | Not confirmed | Yes |
The 3 best Open-Source data quality tools
#2
Elementary
Best forAnalytics engineering teams that build with dbt
dbt-native data observability with an open-source package and CLI plus a paid cloud platform.
Free plan · paid plans quote-only · 30-day free trial of Elementary Cloud · #4 overall
8.4/10
#3
Great Expectations (GX Core)
Best forPython data engineers who want code-defined data tests in pipelines at no license cost
Open-source Python library for testing data with declarative 'Expectations'.
Free plan · #6 overall
7.9/10
Not included, and why
- iceDQ (#1 overall) does not meet the rule: the core product is released under an open-source licence.
- Soda (#2 overall) does not meet the rule: the core product is released under an open-source licence.
- Informatica Data Quality & Observability (#5 overall) does not meet the rule: the core product is released under an open-source licence.
- Acceldata Data Observability Cloud (ADOC) (#7 overall) does not meet the rule: the core product is released under an open-source licence.
- Ataccama ONE (#8 overall) does not meet the rule: the core product is released under an open-source licence.
- BiG EVAL NX³ (#9 overall) does not meet the rule: the core product is released under an open-source licence.
- Qlik Talend Data Quality (#10 overall) does not meet the rule: the core product is released under an open-source licence.
- Anomalo (#11 overall) does not meet the rule: the core product is released under an open-source licence.
- Bigeye (#12 overall) does not meet the rule: the core product is released under an open-source licence.
- Collibra Data Quality & Observability (#13 overall) does not meet the rule: the core product is released under an open-source licence.
- Experian Aperture Data Studio (#14 overall) does not meet the rule: the core product is released under an open-source licence.
- RightData DataTrust (#15 overall) does not meet the rule: the core product is released under an open-source licence.
- Monte Carlo (#16 overall) does not meet the rule: the core product is released under an open-source licence.
- Sifflet (#17 overall) does not meet the rule: the core product is released under an open-source licence.
Buyer's guide: the numbers
How many Open-Source data quality tools publish their prices? 2 of the 3 we ranked publish a price or a free plan; 1 is quote-only: Elementary.
How many Open-Source data quality tools have a free plan? All 3 we ranked have a free plan: Deequ (8.4/10), Elementary (8.4/10), Great Expectations (GX Core) (7.9/10).
Which features set Open-Source data quality tools apart? Data profiling: 1 of 3 (Deequ). ML anomaly detection: 1 of 3 (Elementary). Every tool we ranked confirms custom rule checks, open source, so these do not separate them.
Head-to-head comparisons
Deequ vs Elementary
8.4 vs 8.4 out of 10
Deequ vs Great Expectations (GX Core)8.4 vs 7.9 out of 10
Elementary vs Great Expectations (GX Core)8.4 vs 7.9 out of 10
More data quality tools rankings
Frequently asked questions
What is the best open-source data quality tool?
Deequ ranks first (8.4/10) among the 3 data quality tools that meet this rule: the core product is released under an open-source licence. Elementary is second (8.4/10).
Sources
- Deequ: official pricing page · Deequ GitHub repository · Deequ on Maven Central
- Elementary: official pricing page · Elementary pricing page · Elementary docs: OSS introduction · Elementary docs: volume_anomalies and freshness_anomalies tests · Elementary GitHub repository
- Great Expectations (GX Core): official pricing page · GX Core GitHub repository (fivetran/great_expectations) · Great Expectations blog: An update from Great Expectations · Fivetran press release on GX Core stewardship · GX Core checkpoint actions source · PyPI great-expectations · GX Cloud pricing page · GX docs: Connect to filesystem data · GX API reference: PandasFilesystemDatasource · GX docs: Schema data quality use case · great_expectations GitHub repository
Scores come from published, repeatable data (features, free plan, entry price, pricing transparency). No vendor can pay to change a score. Read the methodology · Disclosure.