Absolutely! While dbt is fantastic for defining tests, Sifflet takes it further with advanced data drift detection and anomaly detection. Our platform uses intelligent monitoring templates that adapt to your data’s behavior, so you can spot unexpected changes like missing rows or unusual values without setting manual thresholds.

Collaboration is key to building a robust observability platform. At Sifflet, our teams work cross-functionally to ensure every part of the platform, from data lineage tracking to real-time metrics collection, aligns with business goals. This teamwork helps us deliver a more comprehensive and user-friendly solution.

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 enriches your data lineage tracking by visually mapping out your dbt models and how they connect across different projects. This is especially useful for teams managing multiple dbt repositories, as Sifflet brings everything together into a clear, centralized lineage view that supports root cause analysis and proactive monitoring.

Looking ahead, Carrefour plans to expand monitoring to more than 1,500 tables, integrate AI-driven anomaly detection, and implement data contracts and SLA monitoring to further strengthen data governance and accountability.

Sentinel and Sage, two of Sifflet’s AI agents, continuously monitor data lineage, usage patterns, and operational metrics to detect issues early. By bundling related alerts, identifying root causes, and suggesting fixes, they reduce downtime and improve overall data reliability. This kind of automated data quality monitoring helps teams stay ahead of incidents and maintain SLA compliance.

Unlike traditional observability tools that focus solely on technical metrics, Sifflet is designed as a business-aware observability platform. It offers features like KPI-to-asset mapping, business-centric data contracts, and end-to-end data lineage tracking. These capabilities ensure that both technical and business teams operate from a shared understanding of data reliability and impact.

Yes, Sifflet is built for cloud-native environments and integrates seamlessly with platforms like Snowflake and Databricks. Its open-source-friendly architecture means you can maintain interoperability while using Sifflet as your central data observability layer.