Snowflake observability integration
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llustration highlighting various aspects of IBM Databand’s Snowflake observability integration

To achieve continuous Snowflake observability and monitoring, IBM® Databand® features seamless Snowflake integration.

Without understanding where and why bottlenecks or anomalies within your Snowflake environment are appearing, data engineers have a hard time keeping people from consuming bad data.

IBM Databand provides continuous observability within your Snowflake warehouse so data teams can detect data incidents with Snowflake tables related to data quality, freshness and volume and resolve them fast.

Use cases Automate detection

Detect issues in the columns using custom and semi-custom queries.

Uncover the unknowns

Detect unexpected anomalies like missing data or spikes in volume.

Guarantee data SLAs

Make sure Snowflake updates within expected data SLAs and with fresh data.

How it works

Databand currently supports monitoring for data-at-rest incidents within your Snowflake data warehouse. This process involves:

1. Create a service account to provide Databand with the necessary permissions

2. Create a new Snowflake monitor in Databand and provide your connection details

Once you've authenticated with Snowflake, you can select the tables you want Databand to monitor. Select entire databases, entire schemas or individual tables. If monitoring an entire database or schema, any future tables added to that database or schema will be automatically added to your monitored tables in Databand.

Capablities

Simplify and centralize your Snowflake table observability with these capabilities.

Data quality alerts

Leverage the power of Databand’s data quality alerts to detect Snowflake table nulls and duplicates before they affect your data consumers. Databand also generates volume alerts to detect unexpected spikes in the volume of your data. Plus, report on data freshness to find out that your tables were updated on time.

Centralized logging

With all Snowflake data quality incidents and their root causes viewable from a centralized console, Databand helps save debugging time. Analyze the historical trends in your data to understand the severity of the issue and automatically detect the root cause. In addition, view the lineage and impact analysis of your Snowflake tables and DAGs.

Take the next step

Implement proactive data observability with IBM Databand today so you can know when there’s a data health issue before your users do.

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