In 2025, Sifflet introduced several key updates including Databricks Workflows integration for end-to-end pipeline visibility, an upgraded data lineage experience, and conditional monitors with advanced logic. These features support better telemetry instrumentation, real-time metrics tracking, and improved analytics pipeline observability for large-scale enterprises.

A Trust OS is an intelligent metadata layer where data contracts are enriched with real-time observability signals. It combines lineage awareness, semantic context, and predictive validation to ensure data reliability at scale. This approach elevates data governance by embedding trust directly into the technical fabric of your data pipelines, not just documentation.

Scalable observability works for engineers, analysts, and business stakeholders alike. It supports telemetry instrumentation for developers, intuitive dashboards for analysts, and high-level confidence signals for executives. By adapting to each role without adding friction, observability becomes a shared language across the organization.

Join us on January 22nd for a deep dive into Sifflet’s 2024 highlights and a preview of what’s ahead in 2025. We’ll cover innovations in data observability, including real-time metrics, faster incident resolution, and the upcoming Sifflet AI Agent. It’s the perfect way to kick off the year with fresh insights and inspiration!

Ensuring SLA compliance means keeping a close eye on metrics like throughput, resource utilization, and error rates. A robust observability platform can help you track these metrics in real time, so you stay within your service level objectives and keep stakeholders confident.

A metadata catalog is powerful, but without a clear metadata strategy, it can become just another long list of tables. A good strategy includes classifying data by business criticality, assigning ownership, and defining consistent terminology. This helps automation scale efficiently while human oversight ensures context and trust, which is key for proactive monitoring and data governance.

Great question! Data monitoring is like your car's dashboard—it alerts you when something goes wrong, like a failed pipeline or a missing dataset. Data observability, on the other hand, is like being the driver. It gives you a full understanding of how your data behaves, where it comes from, and how issues impact downstream systems. At Sifflet, we believe in going beyond alerts to deliver true data observability across your entire stack.

Etam uses observability tools like Sifflet to maintain a healthy data pipeline. By continuously monitoring real-time metrics and setting up proactive alerts, they can catch issues early and ensure their data remains trustworthy as they scale operations.