Anomalo
Autonomous data quality monitoring platform where AI agents monitor, investigate, and report issues across structured and unstructured data.
Best for enterprises that want ML-based monitoring with little rule-writing, including quality checks on unstructured data such as documents.
DQOps
Open-source data quality operations center to profile, automate, and monitor data quality with built-in checks, monitors, and data quality KPI dashboards.
Best for data teams that want to customize built-in checks and track data quality KPIs on dashboards. The open-source version has had no releases since January 2026, so prefer DQOps Cloud.
Sifflet
Data and AI observability control plane with data monitors, end-to-end lineage, a data catalog, and incident management with business context.
Best for data teams that want business users involved in data quality through shared lineage, a catalog, and business-impact alerts.
FirstEigen DataBuck
Zero-code data quality software where AI agents auto-discover rules, validate and reconcile data, and score data trust across sources.
Best for enterprises in regulated industries that need automated data reconciliations and validations across legacy and cloud sources.
Great Expectations
Open-source Python data quality framework, stewarded by Fivetran, with 50+ declarative expectations to validate files, SQL databases, warehouses, and DataFrames.
Best for Python data engineers who want a free, code-first testing library and don't need a managed UI, since GX Cloud is no longer available.
Related integrations
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- Data quality tools for MySQL (23)
- Data quality tools for Azure SQL Database (10)
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- Data quality tools for Amazon Aurora (6)
- Data quality tools for SQLite (6)
- Data quality tools for MongoDB (5)
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