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

Airflow Monitoring Tied to Your Data Quality

Connect Airflow run signals to warehouse freshness and volume, so a late DAG turns into a data incident, not a silent stale table.

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

Airflow monitoring in a data observability context means correlating your DAG and task outcomes with the freshness and volume of the tables they produce. Dataobservability reads orchestration signals alongside warehouse metadata, so when a job fails or runs late you see exactly which downstream tables and dashboards are at risk.

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Why it fits

Teams orchestrating with Airflow who want job health joined to data health.

DAGs linked to tables

A late or failed task is connected to the freshness of the tables it loads.

Beyond job success

A job can succeed and still deliver bad data. Volume and anomaly monitors catch that.

Impact-first alerts

Lineage shows what breaks downstream when a pipeline misses its window.

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.