Databand provides Snowflake observability within your Snowflake warehouse so data teams can identify and resolve data quality issues fast.
No one likes consuming bad data. Databand’s Snowflake observability helps you detect data incidents with your Snowflake tables related to data quality, freshness, and volume so you can eliminate bad data.
Detect issues in the columns using custom and semi-custom queries.
Detect unexpected anomalies like missing data or spikes in volume.
Make sure Snowflake updates within expected data SLAs and with fresh data.
With Databand’s Snowflake observability, data teams can instantly debug incidents in the warehouse and resolve them quickly before creating downstream business impacts.
Leverage the power of Databand’s data quality alerts to detect nulls and duplicates, before it affects your data consumers.
Snowflake observability allows you to generate alerts for incidents like:
Now you can debug incidents easier and resolve them faster. Databand’s Snowflake observability helps data teams quickly view all incidents and their root causes in one place.
Databand automatically monitors the health of our Airflow pipelines so we spend less time debugging, and more time building ML models. Before Databand, 60% of our pipelines had at least one data incident. Now less than 1% of pipelines have incidents. This resulted in a 3X increase in our customers since we can now manage our ML deep learning models at scale.
Databand helps us detect data quality issues faster so we can meet our data SLAs. Without Databand, we didn’t know we had problems until two or three days later – forcing us to backfill the data.
Databand is helping us achieve better pipeline reliability and higher velocity releases for our data products. The platform saves our team a lot of troubleshooting time by providing one holistic view for job monitoring and dependencies so that everyone can see what’s happening in our pipelines.
See how Databand can transform data observability at your organization today.