Discover the best 12 data quality tools that integrate with Azure Data Factory.
By Ari Bajo - Data Engineer turned Writer.
See the full data quality tools list
All 36 data quality, data testing, and data observability tools.
Managed enterprise data platform built on OpenMetadata with data discovery, observability metrics, column-level lineage, governance workflows, and enterprise support.
Best for data teams looking for a fully managed enterprise version of OpenMetadata with dedicated support, advanced governance features, and a wide range of integrations.
Data trust platform with data quality evaluation rules, anomaly detection, data lineage, a data catalog, and master data management.
Best for organizations looking to scale data management initiatives with enterprise master data management, data quality, and data governance.
Leading data observability platform with data monitors, anomaly detection, customizable data quality dashboards, and column-level lineage.
Best for data teams with a big budget looking for a mature and customizable data observability platform that also offers AI observability.
Agentic data observability platform with AI agents for data monitoring, data lineage, and FinOps.
Best for data teams looking for an enterprise data observability platform pivoting to a ChatGPT-like interface for all data management initiatives.
Automated data operations platform with data observability, pipeline observability, end-to-end pipeline lineage, and incident management.
Best for data operations teams looking for end-to-end data pipeline lineage with automated root-cause analysis and integrations with Jira or ServiceNow.
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By Ari Bajo - Data Engineer turned Writer.
Automated data quality monitoring platform with UI-based anomaly detection tests for structured and unstructured data.
Best for data teams looking for a specialized data quality monitoring tool that integrates with specialized and cloud-native data catalog tools.
Lineage-enabled data observability platform with data quality metrics monitoring, anomaly detection, a data catalog, and end-to-end data lineage.
Best for data teams looking to add code-based data observability for a mix of modern and legacy data warehouses and ETLs.
Open-source data quality platform with 150+ Jinja2 SQL-based checks configurable via YAML or a local web UI, data lineage, and incident management. DQOps Cloud adds cloud storage for quality results, hosted Looker Studio dashboards, and team collaboration.
Best for data teams looking for a highly customizable open-source data quality check library with a local web UI and Looker Studio dashboard integrations. DQOps Cloud adds shared dashboards and multi-user collaboration without managing your own infrastructure.
AI-augmented data observability platform with data monitors, column-level data lineage, incident management, and a data catalog.
Best for data analytics teams looking for full-featured data observability, data lineage, and a data catalog in the same tool with integrations for cloud data warehouses.
Unified data trust platform with data monitoring, pipeline monitoring, a data catalog, column-level lineage, and data access control.
Best for data teams looking to combine data observability, a data catalog, and data governance in the same tool.
Building or buying a data tool in 2026?
One email a month — a new market guide and tool list, straight to your inbox. Next up: Data Governance, LLMOps, Data Orchestration.
By Ari Bajo - Data Engineer turned Writer.
Unified data quality and observability platform with anomaly detection, data quality checks, end-to-end data lineage, and pipeline observability.
Best for enterprises looking for unified data quality and observability that integrates with modern data catalogs and issue management tools.
Data pipeline and data warehouse monitoring platform with data pipeline job monitors, schema change monitors, custom SQL monitors, alerts, and task-based data lineage.
Best for data teams looking for end-to-end ETL pipeline monitoring with tasks that span across dbt, Airlfow, Azure Data Factory, Spark, IBM DataStage, and IBM Watsonx Data.
Want to go deeper?
Read the complete data quality tool market guide — features, pricing, and how to choose.
Read the data quality guide