Deequ vs Great Expectations (GX Core)
Verdict: Deequ scores 8.4/10 against 7.9/10 for Great Expectations (GX Core) on our data score for data quality tools.
Both have a free plan.
Deequ and Great Expectations (GX Core) lead on 0 checks each; 5 are even, out of 5 checks (yes/no, price and seats) where we have data for both.
Choose Deequ for spark and Scala/Python engineers validating large datasets in batch pipelines. Choose Great Expectations (GX Core) for python data engineers who want code-defined data tests in pipelines at no license cost.
Scoreboard: 5 checks
| Check | Deequ | Great Expectations (GX Core) |
|---|---|---|
| Free plan | Even | Even |
| Free trial | Even | Even |
| Smaller seat minimum | Even | Even |
| Custom rule checks | Even | Even |
| Open source | Even | Even |
| Total | 0 | 0 |
At a glance
| Deequ | Great Expectations (GX Core) | |
|---|---|---|
| Score | 8.4/10 (#3) | 7.9/10 (#6) |
| Best for | Spark and Scala/Python engineers validating large datasets in batch pipelines | Python data engineers who want code-defined data tests in pipelines at no license cost |
| Free plan | Yes | Yes |
| Paid from (annual) | See vendor | See vendor |
| Paid from (monthly) | — | — |
| Free trial | — | — |
| Minimum seats | 1 | 1 |
| Platforms | Self-hosted (Apache Spark library) | Self-hosted (Python library), Python library (self-hosted), Cloud (SaaS) |
Features
| Tool | Score | Data profiling | Custom rule checks | ML anomaly detection | Data cleansing / correction | Open source |
|---|---|---|---|---|---|---|
| Deequ | 8.4/10 | Yes | Yes | Not confirmed | Not confirmed | Yes |
| Great Expectations (GX Core) | 7.9/10 | Not confirmed | Yes | Not confirmed | Not confirmed | Yes |
- Only Deequ confirms: Data profiling.
Pros and cons
Deequ
Pros- Free and open source (Apache-2.0)
- Scales with Apache Spark for very large datasets
- Automatic constraint suggestion and column profiling
- Supports AWS Data Quality Definition Language (DQDL) rules
- Library only: no UI, scheduling or alerting built in
- Requires Spark; releases are tied to specific Spark versions
- Recent versions (2.1.0+) require Java 11
Great Expectations (GX Core)
Pros- Free and open source (Apache-2.0), about 11.9k GitHub stars
- Large library of built-in Expectations, extensible with custom ones
- Checkpoint actions can notify Slack, Microsoft Teams, email, PagerDuty, Opsgenie or an API endpoint
- Actively released: version 1.25.0 published on PyPI on 2026-11-12
- GX Cloud (the hosted UI) is no longer publicly available, so there is no vendor-hosted option
- Code-first; requires Python skills to set up and maintain
- No built-in ML anomaly detection or data cleansing stated
- Long-term roadmap under Fivetran stewardship not yet detailed
Frequently asked questions
How do Deequ and Great Expectations (GX Core) compare on features and price?
Deequ and Great Expectations (GX Core) lead on 0 checks each; 5 are even, out of 5 checks (yes/no, price and seats) where we have data for both.
Is Deequ better than Great Expectations (GX Core)?
Deequ scores 8.4/10 against 7.9/10 for Great Expectations (GX Core) on our data score for data quality tools. Deequ is best for spark and Scala/Python engineers validating large datasets in batch pipelines; Great Expectations (GX Core) is best for python data engineers who want code-defined data tests in pipelines at no license cost.
Which is cheaper, Deequ or Great Expectations (GX Core)?
Check each vendor's pricing page; one of them does not publish an entry price.
Do Deequ and Great Expectations (GX Core) have free plans?
Deequ: yes. Great Expectations (GX Core): yes.
See the full Data Quality Tools ranking for other options.
Sources
- Deequ: official pricing page · Deequ GitHub repository · Deequ on Maven Central
- 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