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

Redshift Data Monitoring for Freshness and Anomalies

Keep your Redshift clusters honest with monitoring for freshness, volume, schema, and anomalies on every 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

Redshift data monitoring continuously checks your Amazon Redshift tables for freshness, volume, schema, and anomaly issues, and maps lineage across your schemas. Dataobservability connects with read-only access, reads system tables and statistics rather than scanning data, and alerts your team when something breaks.

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Why it fits

Redshift teams who want modern observability on their existing warehouse.

Reads system catalogs

Monitoring leans on Redshift system tables, keeping cluster load minimal.

Full-pillar coverage

Freshness, volume, schema, and anomaly monitors across every schema.

Mapped impact

Lineage shows the downstream reports affected by any break.

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 Airflow monitoring

Live in 15 minutes

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