Looking for the best data quality tools for Databricks Delta Lake? This list covers 4 tools that natively integrate with Databricks Delta Lake — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its Databricks Delta Lake 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.
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.
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.
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Market Guide (7,000 words) · Feature Matrix (73 features) · Integration Matrix (227 integrations)
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By Ari Bajo - Data Engineer turned Writer.