DQOps
Open-source data quality operations center that runs DQOps checks from a Data Factory Web activity that calls its run checks REST endpoint with an API key and returns results to the pipeline.
Best for teams that want free, self-hosted profiling and monitoring checks on Data Factory with an open-source core.
Collate
Managed agentic data platform built on OpenMetadata that connects to Data Factory with a service principal or managed identity and ingests pipelines, recent pipeline runs, and lineage.
Best for teams that want Data Factory pipeline runs and lineage next to their data quality monitors.
Ataccama ONE
Agentic data trust platform that connects with an Azure service principal to read factories, pipelines, datasets, and data flows, deriving attribute-level lineage from Copy activities.
Best for teams that want Data Factory pipeline runs and lineage next to their data quality monitors.
Monte Carlo
Data + AI observability platform that connects with a service principal holding Reader on the factory, collecting pipelines, activities, and run status hourly and deriving lineage from Copy activities.
Best for teams that want Data Factory pipeline runs and lineage next to their data quality monitors.
Acceldata
Autonomous data and AI platform that connects with a service principal or managed identity holding Reader and detects pipelines, runs, and activity metadata on a schedule.
Best for teams that want Data Factory pipeline runs and lineage next to their data quality monitors.
Bigeye
Enterprise AI trust platform that connects with an Azure service principal to read pipeline definitions and 30 days of run history, producing column-level lineage.
Best for teams that want Data Factory pipeline runs and lineage next to their data quality monitors.
Sifflet
Data and AI observability control plane that connects with an Entra ID service principal and a read-only role, reading each pipeline's definition and latest run status without starting or editing pipelines.
Best for teams that want Data Factory pipeline runs and lineage next to their data quality monitors.
Decube
Data observability, quality, and lineage platform that connects with a service principal to read Data Factory jobs, runs, and tasks, and maps lineage to upstream and downstream objects.
Best for teams that want Data Factory pipeline runs and lineage next to their data quality monitors.
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.
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.
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By Ari Bajo - Data Engineer turned Writer.