The revamped 'Test Connection' process for Snowflake observability now provides detailed feedback on missing permissions or policy issues. This makes setup and troubleshooting much easier, especially during onboarding. It helps ensure smooth data pipeline monitoring and reduces the risk of refresh failures down the line.

Data lineage adds context to data observability by linking alerts to their root cause. For example, if a metric suddenly drops, lineage helps trace it back to a delayed ingestion or schema change. This speeds up incident resolution and strengthens anomaly detection. Platforms like Sifflet combine lineage with real-time metrics and data freshness checks to provide a complete view of pipeline health.

After evaluating several data catalog vendors, jobvalley selected Sifflet because of its comprehensive features that addressed both data discovery and data quality monitoring. The platform’s ability to streamline onboarding and support real-time metrics made it the ideal choice for their growing data team.

Great question! A strong data observability platform helps reduce uncertainty in your data pipelines by providing transparency, real-time metrics, and proactive anomaly detection. When your team can trust the data and quickly identify issues, they feel more confident, empowered, and less stressed, which directly boosts team morale and satisfaction.

Traceability is a key pillar of Sifflet’s observability platform. We’ve expanded support for tools like Synapse, MicroStrategy, and Fivetran, and introduced our Universal Connector to bring in any asset, even from AI models. This makes root cause analysis and data lineage tracking more comprehensive and actionable.

At Sifflet, data security is built into the foundation of our data observability platform. We follow three core principles: least privilege, no storage, and single tenancy. This means we only use read-only access, never store your data, and isolate each customer’s environment to prevent cross-tenant access.

As more businesses rely on data to drive decisions, ensuring data reliability is critical. Data observability provides transparency into the health of your data assets and pipelines, helping teams catch issues early, stay compliant with SLAs, and ultimately build trust in their data.