Looking for the best data quality tools for Apache Airflow? This list covers 18 tools that natively integrate with Apache Airflow — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its Apache Airflow integration documentation so you can evaluate support.
By Ari Bajo - Data Engineer turned Writer.
See the full data quality tools list
All 36 data quality, data testing, and data observability tools.
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.
Data + AI observability platform that monitors data pipelines and AI agents in production, with column-level lineage and AI troubleshooting agents.
Best for large data teams ready to invest in a mature, widely adopted platform that monitors both their data and their AI agents.
Autonomous data and AI platform, born in data observability, with AI agents for data monitoring, lineage, governance, and cost optimization.
Best for large enterprises with hybrid or on-premise data estates that want observability, FinOps, and agentic data management from one vendor.
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.
Open-source dbt package by Boomi that adds anomaly detection tests and a CLI-generated data observability report to dbt projects.
Best for dbt teams that want free anomaly detection tests inside their existing dbt project, without a cloud account.
Data quality control layer that validates data where it's used, with checks auto-generated from profiling, anomaly detection, and quality context for AI agents.
Best for enterprises in regulated industries that need high test coverage without hand-writing rules, and on-premise deployment via Kubernetes.
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.
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.
End-to-end data observability platform by Datadog that catches silent data quality issues with ML monitors and column-level lineage to BI dashboards.
Best for modern data stack teams that want monitors running in minutes from the UI, especially if they already use Datadog.
Agentic data quality and observability platform with real-time validators for streams, lakes, and warehouses, end-to-end lineage, and incident management.
Best for data teams that need real-time anomaly detection on data streams like Kafka and Kinesis as well as in warehouses.
AI-native platform combining data observability, data quality checks, and business context, with anomaly detection, end-to-end lineage, and pipeline observability.
Best for enterprises that want data quality and observability in one tool, connected to their data catalog and issue management tools.
Data quality product by Coalesce, built on SYNQ, with UI-based monitors, column-level lineage, and incident workflows next to your transformations.
Best for Coalesce users that want data transformation, a data catalog, and data quality in one product.
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.
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.
Managed agentic data platform built on OpenMetadata, with AI agents, data quality tests, observability metrics, column-level lineage, and governance workflows.
Best for data teams that want OpenMetadata fully managed, with enterprise support, security features, and advanced governance workflows.
Data & AI control plane by Boomi with anomaly detection monitors, column-level lineage, incident management, a data catalog, and AI agents.
Best for dbt-centric analytics teams that want managed monitoring, lineage, and AI agents for triage on top of their dbt tests.
Enterprise AI trust platform built on data observability, with anomaly detection, end-to-end lineage, and governance of the data AI agents use.
Best for large enterprises with modern and legacy data sources that also need to govern which data their AI agents access.
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.
Data observability, quality, and lineage platform that links data monitors to column-level lineage, a data catalog, and access control to show business impact.
Best for data teams that want observability, a data catalog, and data governance in the same tool.
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By Ari Bajo - Data Engineer turned Writer.