Great question! With the launch of Data-Quality-as-Code v2, Sifflet has made it much easier to create and manage monitors at scale. Whether you prefer working programmatically or through the UI, our platform now offers smoother workflows and standardized threshold settings for more intuitive data quality monitoring.

When evaluating an observability platform, look for strong data lineage tracking, real-time metrics collection, anomaly detection capabilities, and broad integrations across your data stack. Features like field-level lineage, ease of setup, and user-friendly dashboards can make a big difference too. At Sifflet, we believe observability should empower both technical and business users with the context they need to trust and act on data.

Absolutely. Containerized environments paired with observability tools like Kubernetes and Prometheus for data enable faster incident detection and response. Features like real-time alerts, dynamic thresholding, and on-call management workflows make it easier to maintain healthy pipelines and reduce downtime.

Sifflet’s AI agent, Sage, performs root cause analysis by combining metadata, query logs, code changes, and historical incidents to build a full narrative of the issue. This speeds up resolution and provides context-rich insights, making it easier to pinpoint and fix data pipeline issues efficiently.

By including Fivetran connectors and source assets in the lineage graph, Sifflet gives you full visibility into where data issues originate. This makes it much easier to perform root cause analysis and resolve incidents faster, improving overall data reliability.

Absolutely! With shared visibility into data flows and transformations, observability platforms foster better communication between data engineers, analysts, and business users. Everyone can see what's happening in the pipeline, which encourages ownership and teamwork around data reliability.

MCP empowers LLMs to use structured inputs like logs and pipeline metadata, making it easier to trace issues across multiple steps. This structured interaction helps streamline root cause analysis, especially in complex environments where traditional observability tools might fall short. At Sifflet, we’re integrating MCP to enhance how our platform surfaces and explains data incidents.

Data observability adds critical context that static contracts lack, such as data lineage tracking, real-time usage patterns, and anomaly detection. With observability tools, teams can proactively monitor contract compliance, detect schema drift early, and ensure SLA compliance before issues impact downstream systems. It transforms contracts from documentation into enforceable, living agreements.