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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BigQuery Data Quality Monitoring, Self-Serve

Self-serve data quality monitoring for BigQuery that watches every dataset for freshness, volume, schema, and anomalies.

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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

BigQuery data quality monitoring continuously checks your BigQuery tables for freshness, volume, schema, and anomaly issues, and maps lineage across your datasets. Dataobservability connects with read-only access, uses BigQuery metadata and table statistics to keep query cost low, and alerts your team when data breaks.

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Why it fits

BigQuery teams who want broad coverage without running up query costs.

Metadata-first on BigQuery

We lean on INFORMATION_SCHEMA and table metadata, so monitoring stays cheap.

Coverage across datasets

Monitors generate automatically across projects and datasets.

Lineage to Looker and more

Column-level lineage spans BigQuery through to your BI layer.

Explore more

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

Live in 15 minutes

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