Discover the best 12 data quality tools that integrate with Hive SQL.
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.
AI-powered data quality platform with auto-generated tests from profiling results, anomaly detection, and data quality context for humans and AI agents.
Best for enterprises in highly regulated industries looking for a scalable data quality platform with on-premise cloud deployments via Kubernetes.
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By Ari Bajo - Data Engineer turned Writer.
Data management platform with source code analysis, data impact reports, column-level data lineage to BI, and data contracts.
Best for data teams looking to prevent data quality incidents with data impact reports integrated within their development lifecycle through Git and PRs.
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.
Comprehensive data observability platform with data validation, data profiling, column-level data lineage, a data catalog, and a business glossary.
Best for enterprises looking for a data quality tool that can be easily self-hosted with a Docker deployment.
Data observability platform with data profiling, metrics, anomaly detection monitors, table-level lineage, and incident management.
Best for data teams looking for efficient and scalable window-based metrics for data warehouses monitoring with integrations with data catalogs and issue management tools.
Data product platform to build data marketplaces with data contracts based on the Open Data Contract Standard (ODCS), the open-source Data Contract CLI, and data policy checks.
Best for organizations looking to build a data product marketplace with data policy checks.
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.
Agentic data observability and FinOps platform for the cloud with integrations with external data quality checks, cost optimization, incident management, and resolution.
Best for data and infrastructure teams that want to combine data quality results with costs and performance insights into one platform with optimization recommendations.
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 the AWS Glue Data Catalog and AWS Glue ETL jobs that want to monitor data quality at rest and in transit, with the possibility to quarantine data.
Want to go deeper?
Read the complete data quality tool market guide — features, pricing, and how to choose.
Read the data quality guide