Yes, it is! Sifflet currently supports Data Sharing to Snowflake, BigQuery, and S3, with more destinations on the way. This makes it easy to integrate Sifflet into your cloud data observability strategy and leverage your existing infrastructure for deeper insights and proactive monitoring.

Modern data catalogs capture four key types of metadata: technical (schemas, formats), business (definitions, KPIs), operational (usage patterns, SLA compliance), and governance (access controls, data classifications). These layers work together to support data quality monitoring and transparency in data pipelines.

Absolutely! Sifflet’s field-level data lineage tracking lets you trace data issues from BI dashboards all the way back to source systems. Its AI agent, Sage, even recalls past incidents to suggest likely causes, making root cause analysis faster and more accurate for data engineers and analysts alike.

Great question! A true data observability platform doesn’t stop at just detecting issues. It guides you through the full lifecycle: monitoring, alerting, triaging, investigating, and resolving. That means it should handle everything from data quality monitoring and anomaly detection to root cause analysis and impact-aware alerting. The best platforms even help prevent issues before they happen by integrating with your data pipeline monitoring tools and surfacing business context alongside technical metrics.

One major challenge was shifting the company culture from being data-aware to truly data-driven. Technically, integrating new observability tools into existing infrastructures and managing the initial investment in time and resources also posed hurdles.

When choosing a data lineage tool, look for easy integration with your data stack, a user-friendly interface for both technical and non-technical users, and complete visibility from data sources to storage. These features ensure effective data observability and support your broader data governance efforts.

Semantic quality monitoring ensures that the data feeding into your AI models is contextually accurate and production-ready. At Sifflet, we're making this process seamless with tools that check for data drift, validate schema, and maintain high data quality without manual intervention.

Absolutely! SQL Table Tracer is designed with a minimal API and modular architecture, making it easy to plug into larger observability platforms. It provides the foundational data needed for building features like data lineage tracking, pipeline health dashboards, and SLA monitoring.