Ataccama ONE
Agentic data trust platform that connects to Synapse with a JDBC string, optionally as an Azure AD service principal, and can push profiling and data quality evaluation down to Synapse.
Best for teams that want profiling and data quality rules on Synapse with governance and MDM in the same platform.
Monte Carlo
Data + AI observability platform that connects to dedicated SQL pools with a read-only db_datareader login, collecting table metadata hourly and running volume, metric, and custom SQL monitors.
Best for teams that want ML monitors and lineage on Synapse with incident management at enterprise scale.
Acceldata
Autonomous data and AI platform that connects to a dedicated SQL pool with a SQL user, service principal, or managed identity, and runs profiling and checks on Spark or pushdown engines.
Best for teams that want profiling and quality policies on Synapse with pushdown or Spark engines.
Qualytics
Data quality control layer that connects to Synapse through the Microsoft JDBC driver with a SQL login or Entra ID service principal, reading schemas and tables to profile and scan them.
Best for teams that want inferred data quality checks on Synapse with checks generated from profiles.
Bigeye
Enterprise AI trust platform that connects to Synapse with a SQL login granted SELECT and VIEW DEFINITION, then profiles tables to set metrics and thresholds.
Best for teams that want automatic metrics and thresholds on Synapse with monitoring next to their other warehouses.
Soda Cloud
AI-native data quality platform built on data contracts that connects to Synapse through the SQL Server driver with a username and password and optional encryption, and runs quality scans.
Best for teams that want data quality checks and contracts as code on Synapse with YAML in Git and CI/CD.
Soda Core
Open-source data contracts engine that connects to Synapse through the SQL Server driver with a username and password and optional encryption, and runs quality scans.
Best for teams that want data quality checks and contracts as code on Synapse with YAML in Git and CI/CD.
Sifflet
Data and AI observability control plane that connects to dedicated SQL pools with an Entra ID service principal mapped to a db_datareader user, reading metadata and running monitor queries.
Best for teams that want monitors with business context on Synapse with lineage shared with business users.
Metaplane by Datadog
Data observability platform by Datadog that connects to Synapse with a SQL login granted SELECT on each monitored schema, optionally through an SSH tunnel.
Best for teams that want monitors set up in minutes on Synapse with Datadog alongside.
Validio
Agentic data quality and observability platform that connects to Synapse with Entra ID client credentials or a SQL login, reading sys catalog and DMV views for lineage, and runs validators on a schedule.
Best for teams that want validators with segmentation on Synapse with scheduled polling.
Decube
Data observability, quality, and lineage platform that connects to Synapse with a login holding db_datareader, VIEW DATABASE STATE, and VIEW DEFINITION, for metadata, profiling, and data quality monitors.
Best for teams that want profiling and data quality monitors on Synapse with a catalog in the same tool.
dbt tests
Data tests built into open-source dbt Core that run as SQL in Azure Synapse through the dbt-azuresynapse adapter: generic tests such as unique and not_null, custom SQL tests, and unit tests.
Best for teams that transform Azure Synapse data with dbt and want free tests next to their models, run in the same CI/CD.
Collate
Managed agentic data platform built on OpenMetadata that connects to Synapse over ODBC with a SQL user or Active Directory authentication and ingests table and view metadata.
Best for teams that want Synapse cataloged with lineage, rather than checks on the data.
Pantomath
AI-powered data operations center with data and pipeline observability, cross-platform pipeline lineage, and automated root-cause analysis.
Best for data operations teams that need to trace failures across pipelines and route incidents to Jira or ServiceNow.
Anomalo
Autonomous data quality monitoring platform where AI agents monitor, investigate, and report issues across structured and unstructured data.
Best for enterprises that want ML-based monitoring with little rule-writing, including quality checks on unstructured data such as documents.
DQOps
Open-source data quality operations center to profile, automate, and monitor data quality with built-in checks, monitors, and data quality KPI dashboards.
Best for data teams that want to customize built-in checks and track data quality KPIs on dashboards. The open-source version has had no releases since January 2026, so prefer DQOps Cloud.
FirstEigen DataBuck
Zero-code data quality software where AI agents auto-discover rules, validate and reconcile data, and score data trust across sources.
Best for enterprises in regulated industries that need automated data reconciliations and validations across legacy and cloud sources.
DQLabs
AI-native platform combining data observability, data quality checks, and business context, with anomaly detection, end-to-end lineage, and pipeline observability.
Best for enterprises that want data quality and observability in one tool, connected to their data catalog and issue management tools.
DataKitchen
Open-source data quality testing and observability suite of two tools, TestGen and Observability, with auto-generated tests, data profiling, and anomaly detection.
Best for cost-conscious data teams that want to start free with Apache 2.0 tools and upgrade to Enterprise, priced per connection and user.
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By Ari Bajo - Data Engineer turned Writer.