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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For teams

Data Quality for Data Teams Who Ship Trusted Numbers

For analytics teams who answer for the numbers: catch broken data before it reaches a dashboard, with lineage that proves what is trustworthy.

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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

Data quality for data teams means analytics and BI teams can trust the numbers they publish. Dataobservability monitors freshness, volume, schema, and anomalies across every table, maps column-level lineage to the dashboards stakeholders read, and alerts the team before a broken metric becomes a hard conversation with finance or the board.

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Why it fits

Analytics engineers and BI teams accountable for the numbers stakeholders see.

Trust before you publish

Freshness and anomaly monitors catch stale or wrong data ahead of the morning report.

Lineage to every dashboard

Know exactly which BI assets a broken table feeds.

One source of reliability

A shared view of data health across analytics and engineering.

Explore more

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

Live in 15 minutes

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