Absolutely! We know that data patterns can shift during holidays or unique business dates. That’s why Sifflet now lets you exclude these dates from alerts by selecting from common calendars or customizing your own. This helps reduce alert fatigue and improves the accuracy of anomaly detection across your data pipelines.

Data distribution deviation happens when the distribution of your data changes over time, either gradually or suddenly. This can lead to serious issues like data drift, broken queries, and misleading business metrics. With Sifflet's data observability platform, you can automatically monitor for these deviations and catch problems before they impact your decisions.

At Sifflet, we’re leveraging MCP to build agents that can observe, decide, and act across distributed systems. By injecting telemetry data, user context, and pipeline metadata as structured resources, our agents can navigate complex environments and improve distributed systems observability in a scalable and modular way.

Sifflet is designed to ensure data quality and reliability, which are critical for AI initiatives. Our observability platform includes features like data freshness checks, anomaly detection, and root cause analysis, making it easier for teams to maintain high standards and trust in their analytics and AI models.

As data pipelines grow more complex, data observability provides the visibility needed to monitor and troubleshoot issues across the full stack. By adopting a robust observability platform, teams can detect anomalies, ensure SLA compliance, and maintain data reliability without relying on manual checks or reactive fixes.

Flow Stopper enables early anomaly detection and integrates with your orchestrator to halt execution when issues are found. This makes it easier to perform root cause analysis before problems escalate and helps prevent incidents that could affect business-critical dashboards or KPIs.

The MCP (Model Context Protocol) Server is a new interface that lets you interact with Sifflet directly from your development environment. It's designed to make data observability more seamless by allowing you to query assets, review incidents, and trace data lineage without leaving your IDE or notebook. This helps streamline your workflow and gives you real-time visibility into pipeline health and data quality.

Data observability enhances a data catalog by adding continuous monitoring, data lineage tracking, and real-time alerts. This means organizations can not only find their data but also trust its accuracy, freshness, and consistency. By integrating observability tools, a catalog becomes part of a dynamic system that supports SLA compliance and proactive data governance.