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.
Soda Cloud
AI-native data quality platform with data contracts written in YAML, UI, or AI, anomaly detection, and AI agents that explain and fix data issues.
Best for data teams that want engineers and business users to agree on data contracts in one place and resolve bad data together.
Soda Core
Open-source data contracts engine (Soda 4) to write data quality checks and contracts in YAML and run them in pipelines and CI/CD.
Best for data engineers who prefer YAML over Python for checks and want them running free inside pipelines and CI/CD.
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.
Related integrations
- Data quality tools for PostgreSQL (30)
- Data quality tools for Microsoft SQL Server (27)
- Data quality tools for MySQL (23)
- Data quality tools for Azure SQL Database (10)
- Data quality tools for MariaDB (9)
- Data quality tools for Amazon Aurora (6)
- Data quality tools for SQLite (6)
- Data quality tools for AlloyDB (5)
Join the monthly newsletter
Evaluating data tools in 2026?
I curate the most comprehensive lists of data tools, up-to-date comparison tables, and actionable market guides for data teams. More useful than a Gartner report. More reliable than asking AI. Next up: Data Governance, Data FinOps, Data Orchestration...
No spam. Unsubscribe anytime.
By Ari Bajo - Data Engineer turned Writer.