Shift-Left Data Quality Tools
Tools focusing on preventing data quality issues with data contracts, data-diff, and CI/CD.
Data Contract CLI
Open-source command-line tool for ODCS data contracts that tests MySQL and MariaDB tables with a username and password, taking host and database from the contract's servers block.
Best for teams that want MySQL data tested against vendor-neutral ODCS data contracts in CI/CD with one command.
Soda Cloud
AI-native data quality platform with data contracts written in YAML, UI, or AI, anomaly detection, and AI agents that explain and fix data issues.
Best for data teams that want engineers and business users to agree on data contracts in one place and resolve bad data together.
Soda Core
Open-source data contracts engine (Soda 4) to write data quality checks and contracts in YAML and run them in pipelines and CI/CD.
Best for data engineers who prefer YAML over Python for checks and want them running free inside pipelines and CI/CD.
Datafold
AI-powered data engineering platform with agents for migrations and code reviews, plus data diff, impact analysis, and anomaly detection.
Best for data teams planning a warehouse migration or wanting data diffs in every pull request to catch regressions before merge.
Entropy Data
Data product marketplace (formerly Data Mesh Manager) where every data product has an owner and data contracts based on the Open Data Contract Standard, from the team behind the open-source Data Contract CLI.
Best for organizations adopting data mesh that want a data product marketplace with ODCS data contracts, policy checks, and test results published from the Data Contract CLI.
Data Testing Tools
Tools focusing on code-based data quality tests and data profiling for SQL tables, files, and DataFrames.
pointblank
Open-source data validation library by Posit that validates MySQL tables through its Ibis backend, installed with the mysql extra.
Best for analysts and engineers who want readable MySQL validation reports, starting free in Python.
Great Expectations
Open-source Python data quality framework, stewarded by Fivetran, with 50+ declarative expectations to validate files, SQL databases, warehouses, and DataFrames.
Best for Python data engineers who want a free, code-first testing library and don't need a managed UI, since GX Cloud is no longer available.
Unified Data Quality Tools
Tools combining data quality, data observability, data lineage, and a data catalog in one product.
Collate
Managed agentic data platform built on OpenMetadata, with AI agents, data quality tests, observability metrics, column-level lineage, and governance workflows.
Best for data teams that want OpenMetadata fully managed, with enterprise support, security features, and advanced governance workflows.
OpenMetadata
Open-source context platform that unifies metadata for humans and AI agents, with data discovery, data quality tests, column-level lineage, and governance.
Best for data teams that want one self-hosted, open-source platform for discovery, data quality, and governance with a wide range of integrations.
Ataccama ONE
Agentic data trust platform with data quality rules, observability, a data catalog, lineage, master data management, and a built-in AI agent.
Best for large organizations that want enterprise data quality, master data management, and data governance from one vendor.
Bigeye
Enterprise AI trust platform built on data observability, with anomaly detection, end-to-end lineage, and governance of the data AI agents use.
Best for large enterprises with modern and legacy data sources that also need to govern which data their AI agents access.
Sifflet
Data and AI observability control plane with data monitors, end-to-end lineage, a data catalog, and incident management with business context.
Best for data teams that want business users involved in data quality through shared lineage, a catalog, and business-impact alerts.
Decube
Data observability, quality, and lineage platform that links data monitors to column-level lineage, a data catalog, and access control to show business impact.
Best for data teams that want observability, a data catalog, and data governance in the same tool.
SelectZero
Self-hosted data observability platform with value-level data validation, column-level lineage, a data catalog, and a business glossary.
Best for enterprises that cannot let data leave their environment and want one self-hosted tool, deployed with Docker.
Data Observability Tools
Tools focusing on detecting data quality issues with data monitors and anomaly detection.
Monte Carlo
Data + AI observability platform that monitors data pipelines and AI agents in production, with column-level lineage and AI troubleshooting agents.
Best for large data teams ready to invest in a mature, widely adopted platform that monitors both their data and their AI agents.
Acceldata
Autonomous data and AI platform, born in data observability, with AI agents for data monitoring, lineage, governance, and cost optimization.
Best for large enterprises with hybrid or on-premise data estates that want observability, FinOps, and agentic data management from one vendor.
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.
Qualytics
Data quality control layer that validates data where it's used, with checks auto-generated from profiling, anomaly detection, and quality context for AI agents.
Best for enterprises in regulated industries that need high test coverage without hand-writing rules, and on-premise deployment via Kubernetes.
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.
Metaplane by Datadog
End-to-end data observability platform by Datadog that catches silent data quality issues with ML monitors and column-level lineage to BI dashboards.
Best for modern data stack teams that want monitors running in minutes from the UI, especially if they already use Datadog.
Coalesce Quality
Data quality product by Coalesce, built on SYNQ, with UI-based monitors, column-level lineage, and incident workflows next to your transformations.
Best for Coalesce users that want data transformation, a data catalog, and data quality in one product.
Lightup
Enterprise data observability platform with AI anomaly detection, no-code data quality checks, and an agentic interface that works with Claude and Gemini.
Best for enterprises that want business users to set up no-code checks at scale, with integrations for data catalogs and issue management tools.
Related integrations
- Data quality tools for PostgreSQL (30)
- Data quality tools for Microsoft SQL Server (27)
- Data quality tools for Azure SQL Database (10)
- Data quality tools for MariaDB (9)
- Data quality tools for Amazon Aurora (6)
- Data quality tools for SQLite (6)
- Data quality tools for AlloyDB (5)
- Data quality tools for MongoDB (5)
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By Ari Bajo - Data Engineer turned Writer.