Discover the best 11 data quality tools that integrate with ClickHouse.
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.
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.
Managed data observability platform built on the Elementary OSS dbt package with advanced anomaly detection monitors, column-level lineage, incident management, a data catalog, and AI agents.
Best for data analytics teams using dbt looking for a managed observability platform with advanced anomaly detection, team collaboration, and AI-powered issue resolution.
Open-source dbt package to add data observability to dbt projects with anomaly detection tests and a local data observability report generated via CLI.
Best for data analytics teams using dbt looking to add anomaly detection monitors to their existing dbt codebase without a cloud account.
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.
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.
Managed data quality platform with built-in metrics to write data contracts (using YAML, UI, or AI), anomaly detection and AI agents to clean data.
Best for data teams looking to embed tests at every pipeline stage, collaborate with business users to quarantine and fix bad data, and integrate with data enterprise catalogs.
Open-source Python library and CLI to write and run data contracts in YAML using SodaCL with integrations for data warehouses, databases and query engines.
Best for data engineering teams looking for a YAML-based OSS data testing library that embeds directly in pipelines and CI/CD workflows.
End-to-end data observability platform with data monitors and column-level lineage from data sources to BI dashboards.
Best for data analytics teams with a modern data stack looking to quickly add anomaly detection monitors through the UI.
Real-time data observability platform with window-based data validators, end-to-end data lineage, and incident management.
Best for data teams looking for real-time anomaly detection in data streams, lakes, and warehouses.
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.
Open-source Python library with 50+ declarative expectations to validate data in files, SQL databases, data warehouses, and in-memory DataFrames.
Best for data engineering teams looking for a code-first OSS data testing library with a large built-in expectation library and Python extensibility.
Data observability product by Coalesce after having acquired SYNQ with UI-based data monitors, column-level lineage, and incident management workflows.
Best for Coalesce users that want to unify data transformation, data catalog and data quality in one product.
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.
Want to go deeper?
Read the complete data quality tool market guide — features, pricing, and how to choose.
Read the data quality guide