Looking for the best data quality tools for Google BigQuery? This list covers 29 tools that natively integrate with Google BigQuery — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its Google BigQuery 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.
Cloud-native data validation CLI with YAML-based data quality checks for BigQuery tables and GCS structured data.
Best for data teams looking for a BigQuery-native solution to write reusable SQL checks and consume data quality outputs programmatically.
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 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.
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.
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.
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.
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.
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, 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.
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.
Data pipeline and data warehouse monitoring platform with job pipeline monitors, data monitors, and task-based data lineage.
Best for data teams looking for end-to-end ETL pipeline monitoring with tasks that span across dbt, Airlfow, Spark, IBM DataStage, and IBM Watsonx Data.
Agentic data observability and FinOps platform for the cloud with integrations with external data quality checks, cost optimization, and incident management.
Best for data teams that want to combine in one platform data quality results with costs and performance recommendations.
Open-source data testing and observability platform with automated test generation, data profiling, and anomaly detection.
Best for data teams looking for a cost-effective data testing and observability solution that prices per database connection and user.
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.
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.
Data product platform to build data marketplaces with data contracts based on the Open Data Contract Standard (ODCS).
Best for organizations looking to build a data product marketplace with data policy checks.
Open-source dbt validation toolkit and managed platform with data-diff, data impact reports and column-level data lineage.
Best for data analytics teams using dbt looking to validate code changes with data impact reports during PR reviews.
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.
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 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.
AI-augmented data observability platform with data monitors, end-to-end data lineage, incident management with business context, and a data catalog.
Best for data teams looking to collaborate with business users through integrated data observability, data lineage, and a data catalog for cloud data 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.
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.
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By Ari Bajo - Data Engineer turned Writer.