Looking for the best data quality tools for Dremio? This list covers 9 tools that natively integrate with Dremio — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its Dremio 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.
Open-source Python library with 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.
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.
ML-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.
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.
Data observability platform with data profiling, metrics, anomaly detection monitors, and incident management.
Best for data teams looking for scalable window-based metrics for data warehouses with integrations with data catalogs and issue management tools.
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 data contracts within data pipeline steps, collaborate with business users to fix bad data, and integrate with data 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.
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.
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.
Managed data observability platform 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 team collaboration features and AI-powered issue resolution.
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By Ari Bajo - Data Engineer turned Writer.