We’re focused on solving the next generation of challenges, like hybrid environments, end-to-end data lineage tracking, and scaling data trust. Whether it's batch data observability or real-time pipeline monitoring, our mission is to help organizations build resilient, transparent, and future-proof data stacks.

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.

Sentinel intelligently analyzes metadata like data lineage and schema changes to recommend what really needs monitoring. By focusing on high-impact areas, it cuts down on noise and helps teams manage alert fatigue while optimizing monitoring costs.

Great question! MCP allows large language models to access structured external context like pipeline metadata, logs, and diagnostics tools. At Sifflet, we use MCP to enhance data observability by enabling intelligent agents to monitor, diagnose, and act on issues across complex data pipelines in real time.

Data lineage tracking provides transparency into how data moves and transforms across systems. In hybrid architectures, it helps enforce governance by showing where data comes from, who owns it, and how changes impact downstream consumers, making compliance and audit logging much easier.

With Custom Metadata, you can tag any asset, monitor, or domain in Sifflet using flexible key-value pairs. This makes it easier to organize and route data based on your internal logic, whether it's ownership, SLA compliance, or business unit. It's a big step forward for data governance and helps teams surface high-priority monitors more effectively.

One standout example is their ad inventory forecasting tool. By embedding real-time metrics into internal tools, sales teams can plan campaigns more precisely and avoid last-minute scrambles. It’s a great case of using data to improve both accuracy and efficiency.

When evaluating observability tools, consider factors like ease of setup, support for real-time metrics, data freshness checks, and integration with your existing stack. Look for platforms that offer strong data pipeline monitoring, business context in alerts, and cost transparency. Tools like Sifflet also provide fast time-to-value and support for both batch and streaming data observability.