Dataobservability
How it works Features
Data observability for Snowflake dbt data observability BigQuery data quality Databricks data observability Redshift data monitoring Airflow monitoring For data engineers For analytics teams Data reliability engineering
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Integration

Databricks Data Observability for the Lakehouse

Monitor your Databricks lakehouse for freshness, volume, schema, and anomalies across Delta tables and Unity Catalog.

See how it works
SNOWFLAKE · PROD
247 tables |
Break a monitor:

Alerted #data-eng 0.8s ago.

Downstream impact · consumers at risk

INCIDENT #1042 OPEN · owner @you

In one paragraph

Databricks data observability means monitoring your Delta tables and lakehouse for freshness, volume, schema, and anomaly issues, plus lineage across Unity Catalog. Dataobservability connects with read-only access, uses table and catalog metadata to keep cluster cost low, and alerts the data team the moment a table breaks.

// INTEGRATION

Why it fits

Lakehouse teams on Databricks who need observability without heavy cluster spend.

Built for the lakehouse

Monitors cover Delta tables and read Unity Catalog metadata for lineage.

Low cluster footprint

Metadata-first checks avoid expensive full-table scans.

Alerts that group

Related anomalies collapse into one incident, routed to Slack or PagerDuty.

Explore more

Data quality tools Data quality monitoring Data lineage Pipeline monitoring Data observability for Snowflake dbt data observability BigQuery data quality Redshift data monitoring Airflow monitoring

Live in 15 minutes

Connect your warehouse and watch monitors generate across every table. Transparent pricing, no credit card.