Looking for the best data quality tools for Azure Synapse? This list covers 16 tools that natively integrate with Azure Synapse — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its Azure Synapse 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.
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 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.
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.
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.
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.
Evaluating data quality tools?
Market Guide (7,000 words) · Feature Matrix (73 features) · Integration Matrix (227 integrations)
Join the Newsletter
One email a month — a new tool list, comparison matrix, and market guide, straight to your inbox. Next up: Data Governance, LLMOps, Data Orchestration.
By Ari Bajo - Data Engineer turned Writer.