Data Quality Management Software for Engineers
Your pipelines pass validation. Your dashboards break anyway. DataHub gives platform engineers automated assertions, AI-driven anomaly detection, and full observability across every source.
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Deploy assertions across thousands of datasets without manual rules
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Catch anomalies before they surface in a stakeholder meeting
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Native support for Snowflake, BigQuery, Redshift, and Databricks
See DataHub monitor your data live
Request a demo scoped to your stack, not a generic walkthrough.
What does bad data quality actually cost you?
Reactive data quality means your team investigates failures after the damage is done. The cost is not just broken dashboards, it is trust, time, and audit exposure.
Failures you find in standup
Assertions pass at ingestion. Downstream tables break hours later. You spend the morning explaining what you did not control.
Coverage gaps at scale
Manual rules do not grow with your data. New datasets go unmonitored until something breaks in production.
No lineage when it matters
An incident surfaces. You need to know what is downstream and who is affected. Without column-level lineage, that answer takes hours.
Thresholds that cry wolf
Static rules generate noise. Engineers tune out alerts. The one real failure gets missed in the flood of false positives.
There is a better way to monitor data quality, one that scales with your stack and learns from your data.
Better data quality management tools, built for scale
DataHub replaces manual rule maintenance with automated assertions, ML-driven detection, and a unified view of data health across your entire platform.
Deploy checks across thousands of datasets
Monitoring Rules let you apply assertions in bulk using search predicates, by domain, platform, or schema. New datasets matching your criteria are covered automatically as they are added.
- Seven assertion types: freshness, volume, SQL, field, schema, and more
- Bulk deployment via domain, platform, or schema predicates
- Auto-coverage as new datasets enter your catalog
Stop tuning thresholds manually
Smart Assertions use ML to learn seasonal patterns, dynamic distributions, and statistical baselines in your data. Anomalies are flagged without manual threshold configuration or constant rule maintenance.
- ML-based pattern learning with seasonal trend detection
- Automatic threshold adjustment as data distributions shift
- Handles weekly seasonality and complex statistical patterns
Formalize quality expectations upstream
Data Contracts let producers and consumers agree on schema, freshness, and quality expectations before data moves downstream. Violations surface immediately, not after the fact.
- Define schema, freshness, and field-level quality expectations
- Contract violations trigger alerts before downstream impact
- Linked to column-level lineage for full incident context
Unified observability across your platform
The DataHub Observe dashboard surfaces assertion results, incident history, and health scores across every dataset in one place. Your team sees the full picture without switching tools.
- Dataset health scores aggregated from all assertion results
- Incident timeline linked to column-level lineage
- Slack and PagerDuty routing for critical assertion failures
How data quality software works in practice
Three steps from connection to active monitoring. Works with the infrastructure you already run.
Connect your sources
- Ingest from Snowflake, BigQuery, Redshift, and Databricks
- 80+ pre-built connectors, no pipeline rebuilds required
- dbt, Airflow, and Spark lineage ingested automatically
Contextualize your data
- Apply Monitoring Rules by domain, platform, or schema pattern
- Smart Assertions learn baselines from your actual data history
- Data Contracts formalize expectations between producers and consumers
Activate monitoring
- Assertion failures route to Slack, PagerDuty, or your incident tool
- Column-level lineage shows downstream impact within seconds
- Health scores update continuously as new assertion results arrive
Data quality solutions built for enterprise scale
DataHub deploys in your environment, integrates with your existing stack, and meets the security requirements your organization already enforces.
Data quality monitor and deployment options
- Self-hosted on AWS, GCP, Azure, or on-premises Kubernetes
- DataHub Cloud for fully managed deployment with SLA guarantees
- Apache 2.0 open source core with no vendor lock-in
- GraphQL and OpenAPI for programmatic integration with existing tooling
- Role-based access control with SSO and SCIM provisioning
Platform coverage and integrations
- Snowflake, BigQuery, Redshift, Databricks, and Spark
- dbt Core and dbt Cloud with test result ingestion
- Airflow, Prefect, and Dagster for pipeline-level lineage
- Looker, Tableau, and Power BI for BI-layer lineage and impact analysis
- Slack and PagerDuty for alert routing and incident management
What engineering teams say about DataHub
Gartner Peer Insights
Verified reviewer
"DataHub gave us the lineage and data quality visibility we needed across our Snowflake environment. We can now trace failures to their source and understand downstream impact before stakeholders are affected."