data quality monitoring

Anomalo provides an automated data quality monitoring platform for the enterprise. Its agentic AI suite autonomously monitors, investigates, surfaces, and reports on data quality, including table observability, data quality checks, data insights, conversational analytics, and data documentation. It serves industries including telecommunications, media, financial services, retail, healthcare, and energy.

agentic AIalertingdata lineagedata observabilityno-code configurationroot cause analysisunsupervised machine learning

Coalesce is the data operating layer where humans and AI agents build, govern, and observe data together in one governed platform. It brings Transform, Catalog, and Quality into a single system, offering agentic data engineering with AI inside existing workflows connected through MCPs, CLI, and APIs, with human verification of results.

AI agentsCLI toolingData CatalogingData governancedata lineagedata migrationdata transformationMCP integrationmetadata management

Decube is a data observability platform that connects data quality monitoring, column-level lineage, and ownership to help teams investigate data issues and understand their business impact. It provides a trusted data context layer for teams and AI, combining lineage, quality, governance, and AI in one platform.

AI governanceanomaly detectioncolumn-level lineagedata catalog and discoverydata classificationdata contractsData governancedata observabilitydata productsimpact analysisincident managementnatural language to SQL