iceDQ vs Great Expectations (GX Core)
Verdict: Great Expectations (GX Core) scores 7.9/10 against 7.8/10 for iceDQ on our data score for data quality tools.
Great Expectations (GX Core) has a free plan; iceDQ does not.
Great Expectations (GX Core) leads on 2 checks and iceDQ on 1; 2 are even, out of 5 checks (yes/no, price and seats) where we have data for both.
Choose iceDQ for enterprise QA and data teams testing ETL pipelines, migrations and BI reports. 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 | iceDQ | Great Expectations (GX Core) |
|---|---|---|
| Free plan | Leads | |
| Free trial | Leads | |
| Smaller seat minimum | Even | Even |
| Custom rule checks | Even | Even |
| Open source | Leads | |
| Total | 1 | 2 |
At a glance
| iceDQ | Great Expectations (GX Core) | |
|---|---|---|
| Score | 7.8/10 (#1) | 7.9/10 (#6) |
| Best for | Enterprise QA and data teams testing ETL pipelines, migrations and BI reports | Python data engineers who want code-defined data tests in pipelines at no license cost |
| Free plan | No | Yes |
| Paid from (annual) | See vendor | See vendor |
| Paid from (monthly) | — | — |
| Free trial | Free trial offered via demo request (length not stated) | — |
| Minimum seats | 1 | 1 |
| Platforms | Cloud (SaaS on AWS), On-premises (self-managed) | 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 |
|---|---|---|---|---|---|---|
| iceDQ Featured partner | 7.8/10 | Yes | Yes | Yes | No | No |
| Great Expectations (GX Core) | 7.9/10 | Not confirmed | Yes | Not confirmed | Not confirmed | Yes |
- Only iceDQ confirms: Data profiling, ML anomaly detection.
- Only Great Expectations (GX Core) confirms: Open source.
Pros and cons
iceDQ
Pros- Connectors page lists 150+ native and SQL-based connectors, including Snowflake, Databricks, Google BigQuery, SAP HANA, Salesforce, Kafka and mainframe AS400
- Tests BI reports against their source data; the BI testing page names Power BI, Tableau, Qlik Sense, MicroStrategy, SAP BusinessObjects and IBM Cognos, and Power BI testing supports XMLA and native DAX queries
- File testing covers CSV, JSON, XML, Excel, fixed-width, Parquet, Avro and mainframe EBCDIC/COBOL files, read from S3, ADLS Gen2, Google Cloud Storage, FTP/SFTP and SharePoint
- Integrations page lists 20+ pre-built integrations: Jenkins, Bamboo, Azure DevOps, GitHub Actions, Airflow, Autosys, Control-M, Jira, ServiceNow, HP ALM, Xray and Zephyr
- Offered as SaaS on AWS or self-managed on-premises; the vendor says the customer controls data access in both
- No published prices; the license is described only as consumption-based per user per server or enterprise, and every page leads to 'Book a Demo'
- No free plan; the 'Free Trial' card on the homepage links to the demo request form and gives no trial length or terms
- Many connectors cost extra: the licensing policy lists JSON, XML, Parquet, Avro, REST API, GraphQL, Salesforce and Kafka as premium data connectors, Power BI, Tableau Cloud and Qlik Cloud as premium BI connectors, and Azure DevOps, GitHub Actions and Jira as premium integrations
- For Tableau and Qlik, the connectors page and licensing policy list only Tableau Cloud and Qlik Cloud; Cognos is named on the BI testing page but not in the connector list
- Aimed at data teams rather than API developers: there is no load testing or API mocking, and the vendor itself says it checks the data in API responses rather than replacing functional tools like Postman
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 iceDQ and Great Expectations (GX Core) compare on features and price?
Great Expectations (GX Core) leads on 2 checks and iceDQ on 1; 2 are even, out of 5 checks (yes/no, price and seats) where we have data for both.
Is iceDQ better than Great Expectations (GX Core)?
Great Expectations (GX Core) scores 7.9/10 against 7.8/10 for iceDQ on our data score for data quality tools. iceDQ is best for enterprise QA and data teams testing ETL pipelines, migrations and BI reports; 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, iceDQ or Great Expectations (GX Core)?
Check each vendor's pricing page; one of them does not publish an entry price.
Do iceDQ and Great Expectations (GX Core) have free plans?
iceDQ: no. Great Expectations (GX Core): yes.
See the full Data Quality Tools ranking for other options.
Sources
- iceDQ: official pricing page · iceDQ homepage · iceDQ platform overview (deployment and license FAQ) · iceDQ features · iceDQ connectors · iceDQ integrations · iceDQ licensing policy · iceDQ ETL testing · iceDQ data testing tool · iceDQ data reconciliation · iceDQ BI report testing · iceDQ Power BI testing · iceDQ file testing · iceDQ data monitoring · iceDQ data observability · iceDQ API data testing · iceDQ about us
- 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