Looking for the best data quality tools for Google Cloud SQL? This list covers 3 tools that natively integrate with Google Cloud SQL — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its Google Cloud SQL 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 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.
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.
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.
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By Ari Bajo - Data Engineer turned Writer.