Discover the best 4 data quality tools that integrate with SQLite.
By Ari Bajo - Data Engineer turned Writer.
See the full data quality tools list
All 36 data quality, data testing, and data observability tools.
Managed enterprise data platform built on OpenMetadata with data discovery, observability metrics, column-level lineage, governance workflows, and enterprise support.
Best for data teams looking for a fully managed enterprise version of OpenMetadata with dedicated support, advanced governance features, and a wide range of integrations.
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.
Open-source Python library with 50+ 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.
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.
Want to go deeper?
Read the complete data quality tool market guide — features, pricing, and how to choose.
Read the data quality guide