Discover the best 20 data quality tools that integrate with Amazon Athena.
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 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.
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By Ari Bajo - Data Engineer turned Writer.
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.
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.
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.
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.
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.
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.
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.
Proactive data quality platform with data diff tests, data impact reports, column-level lineage, and data monitors.
Best for data teams looking for data impact reports in PRs to validate code changes and automate data migrations with SQL translation and data reconciliation tests.
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.
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.
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.
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.
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.
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 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.
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.
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 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.
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