Looking for the best data quality tools for Azure SQL Database? This list covers 9 tools that natively integrate with Azure SQL Database — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its Azure SQL Database 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.
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.
Open-source data quality testing and observability platform with data quality checks, monitors, data lineage with Marquez, and data quality dashboards.
Best for data teams looking to customize built-in data quality checks and data quality dashboards with Looker Studio to monitor data quality KPIs.
Real-time data observability platform for data lakes with anomaly detection, data health reports, and incident management.
Best for data teams looking for data observability for data lakes and data lakehouses with native support for Apache Iceberg, Hudi, and Delta Lake.
Open-source data testing and observability platform with automated test generation, data profiling, and anomaly detection.
Best for data teams looking for a cost-effective data testing and observability solution that prices per database connection and user.
Managed data quality platform built on the open-source Deequ framework with data quality rulesets, scheduling, data quality dashboards, and anomaly detection.
Best for data teams using AWS Glue Data Catalog and ETL jobs that want to monitor data quality at rest and in transit, with the possibility to quarantine data.
Managed enterprise data platform built on OpenMetadata with data discovery, observability metrics, column-level lineage, and governance workflows.
Best for data teams looking for a fully managed enterprise version of OpenMetadata with dedicated support, security features, and advanced governance worflows.
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.
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By Ari Bajo - Data Engineer turned Writer.