Absolutely! With Sifflet’s declarative framework, you can programmatically declare assets from tools like Salesforce, SAP, or HubSpot, even if they aren’t natively integrated. This helps you maintain a complete and unified view of your data ecosystem for better data governance.

Great question! As AI and real-time data products become more widespread, data observability is crucial for ensuring data reliability, privacy, and performance. A strong observability platform helps reduce data chaos by monitoring pipeline health, identifying anomalies, and maintaining SLA compliance across increasingly complex data ecosystems.

Absolutely! Sifflet offers both SaaS and self-managed deployment models. With the self-managed option, you can run the platform entirely within your own infrastructure, giving you full control and helping meet strict compliance and security requirements.

By integrating real-time data quality monitoring directly into your orchestration layer, Flow Stopper gives Data Engineers the ability to stop the flow when something looks off. This means fewer broken pipelines, better SLA compliance, and more time spent on innovation instead of firefighting.

Sifflet significantly cut down Meero's troubleshooting time by enabling faster root cause analysis. With real-time alerts and automated anomaly detection, the data team was able to identify and resolve issues in minutes instead of hours, saving up to 50% of their time.

The future of data observability is all about scale and responsibility. With AI adoption growing and regulations tightening, businesses need observability tools that can handle unstructured data, ensure SLA compliance, and support security observability. At Sifflet, we're already helping customers monitor ML models and enforce data contracts, and we're excited about building self-healing pipelines and extending observability to new data types.

Data lineage tracking is a game-changer for data teams. It helps you understand how data flows through your systems and what downstream processes depend on it. When something breaks, lineage reveals the blast radius—so instead of just knowing a table is late, you’ll know it affects marketing campaigns or executive reports. It’s a critical part of any observability platform that wants to move from reactive to proactive.

Absolutely! In fact, data monitoring is often a key feature within a broader data observability solution. At Sifflet, we combine traditional monitoring with advanced capabilities like data profiling, pipeline health dashboards, and data drift detection so you get both alerts and insights in one place.