Built for platform teams

Data Quality Management Software Built for Scale

Your pipelines pass. Your dashboards break anyway. DataHub's data quality management software catches failures before your stakeholders do.

  • Detect anomalies before they reach downstream consumers
  • Run assertions in-network, no data leaves your environment
  • Monitor hundreds of datasets from a single health dashboard
Request a Demo

See DataHub catch issues in your stack

Talk to a DataHub engineer about your specific environment.

Trusted by modern data teams
The real cost

What do data quality management tools miss?

Most tools alert after the damage is done. Here is what that costs platform engineers every week.

Failures you find in standup

A pipeline passed. A dashboard broke. You spend the morning tracing it back through three systems you don't own.

Alerts with no context

A threshold fires. You don't know if it's a real incident or a Monday volume spike. You investigate anyway.

Contracts no one enforces

SLAs exist in a doc somewhere. When data arrives late or malformed, there is no automated signal, just a complaint.

Scale breaks manual checks

You can write assertions for 10 datasets. You can't maintain them for 500. Coverage degrades as the catalog grows.

Data quality management software

A better way to manage data quality

DataHub gives platform engineers the assertion coverage, anomaly detection, and scale-out monitoring that legacy tools can't deliver.

Data quality software Assertions for every failure mode

DataHub supports freshness, volume, column, schema, and custom SQL assertions, validated against Snowflake, Redshift, BigQuery, and Databricks natively.

  • Freshness SLAs validated via audit logs or watermark columns
  • Column checks: nullness, uniqueness, range, and custom metrics
  • Schema assertions detect breaking changes before they propagate

AI detection Smart assertions learn your baseline

ML-based anomaly detection builds a baseline from historical data, accounts for seasonality, and alerts only when something genuinely deviates, reducing false positives without manual tuning.

  • Seasonality-aware: learns Monday volume differs from Friday
  • Historical backfill gives predictions from day one
  • Tunable sensitivity and exclusion windows cut alert noise

Data quality monitor Monitor at scale with one rule

Define a rule once. DataHub applies Smart Assertions across every dataset matching your search predicate, by domain, platform, or schema. New datasets are covered automatically.

  • Apply assertions across hundreds of datasets from one rule
  • New datasets matching the rule get monitors automatically
  • Data Health Dashboard shows coverage and status at a glance

Lineage impact Trace failures to root cause fast

When an assertion fails, DataHub's lineage graph shows you exactly which downstream dashboards, models, and pipelines are affected, so you triage in minutes, not hours.

  • Column-level lineage traces impact to specific fields
  • Incident timeline links failures to upstream change events
  • Notify asset owners automatically when their data is affected
How it works

Connect, validate, and activate

Three steps from your existing stack to full assertion coverage. No pipeline rebuilds required.

Step 1: Connect your sources

Ingest from Snowflake, BigQuery, Databricks, and dbt
Metadata stays in your environment, nothing is copied out
API-first ingestion works with your existing orchestration

Step 2: Configure your data quality monitoring tool

Build assertions for freshness, volume, schema, and columns
Enable Smart Assertions to auto-detect anomalies at scale
Apply monitoring rules across domains without manual setup

Step 3: Activate and respond

Route alerts to Slack, PagerDuty, or your incident workflow
Use lineage to identify root cause and downstream impact
Track resolution status from the Data Health Dashboard
Enterprise ready

Data quality solutions built for enterprise scale

DataHub deploys in your environment, integrates with your stack, and meets your security requirements out of the box.

Deployment and security

Self-hosted or managed cloud deployment options
Role-based access control with fine-grained policies
SSO via SAML, OIDC, and Okta integration
Audit logs for every metadata and policy change

Integrations

Snowflake, BigQuery, Redshift, Databricks, and Spark
dbt, Airflow, Fivetran, and Kafka for pipeline context
Looker, Tableau, and Power BI for downstream visibility
Slack and PagerDuty for incident routing

Scale and extensibility

GraphQL and REST APIs for custom tooling and automation
Handles thousands of datasets across multiple platforms
Open source core with enterprise support and SLAs available
Python SDK for programmatic assertion management
Peer validation

Trusted by modern data teams

Gartner Peer Insights

Verified review

Outcome

Reduced time spent on data incident triage

"DataHub gave our platform team visibility we didn't have before. We can trace a broken dashboard back to the source in minutes, not hours. The assertion coverage across our Snowflake environment is something we couldn't build ourselves."

Senior Data Engineer

Financial services, enterprise

FAQ

Frequently asked questions about data quality management software

DataHub executes assertions directly against your data warehouse or lakehouse using native query pushdown. For Snowflake, BigQuery, Redshift, and Databricks, the assertion logic runs as a query inside your environment. No data is extracted or transmitted to DataHub infrastructure. Metadata about assertion results, pass or fail status, and timestamps, is what gets stored in the DataHub catalog.
It depends on your environment's complexity and how many sources you're connecting. Most teams complete initial ingestion and configure their first assertions within a few days. Monitoring rules that apply assertions across entire domains take longer to tune, but the coverage scales without proportional engineering effort. DataHub's implementation team works with you to scope the rollout based on your catalog size and priority datasets.
dbt tests and Great Expectations are assertion frameworks that run at pipeline execution time. DataHub is a metadata platform that adds assertion monitoring, anomaly detection, lineage context, and cross-platform coverage on top of your existing stack. DataHub can ingest dbt test results and surface them alongside its own assertions, so you get a unified view of data health across sources that dbt doesn't touch, including raw ingestion layers, BI tools, and streaming platforms.
Smart Assertions build a statistical model of each metric's historical behavior, including day-of-week patterns, monthly cycles, and trend direction. When a value falls outside the expected range for that specific time window, an alert fires. Accuracy depends on the quality and length of your historical data. DataHub supports historical backfill so the model has signal from day one. You can adjust sensitivity thresholds and define exclusion windows for known anomalous periods, such as end-of-quarter loads, to reduce false positives.
Yes. DataHub is open source under the Apache 2.0 license and can be deployed entirely within your own infrastructure, including air-gapped environments. The managed cloud offering is available for teams that prefer not to operate the platform themselves. For financial services, healthcare, and other regulated industries, self-hosted deployment means metadata never leaves your environment. DataHub's enterprise tier includes support for SAML SSO, role-based access control, and audit logging to support compliance requirements.

Ready to catch data failures before standup?

DataHub gives your platform team assertion coverage, anomaly detection, and lineage-powered triage across your entire data stack. You'll speak with a DataHub engineer about your specific environment.

Request a Demo Learn about the platform
No data leaves your environment Apache 2.0 open source Scoped to your stack, not a generic walkthrough