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.

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.

Sifflet offers end-to-end data observability that goes beyond anomaly detection. It monitors data pipelines, tracks field-level data lineage, and provides full context around incidents. With AI agents and real-time metrics, Sifflet helps teams understand root causes and business impact, not just surface-level issues.

Yes, and that’s one of the things we’re most excited about! Sifflet empowers non-technical users to define custom monitoring rules and participate in data quality efforts without needing to write dbt code. It’s all part of building a culture of shared responsibility around data governance and observability.

ETL (Extract, Transform, Load) transforms data before storing it, while ELT (Extract, Load, Transform) loads raw data first, then transforms it. With modern cloud storage, ELT is often preferred for its flexibility and scalability. Whichever method you choose, pairing it with strong data pipeline monitoring ensures smooth operations.

Sifflet uses Sentinel, an AI-powered agent, to automate data quality monitoring. It scans your metadata and data samples to suggest monitors for data freshness checks, schema validation, and more. This means you get proactive monitoring with minimal manual setup, making it easier to scale your observability efforts.

Absolutely! Sifflet monitors key data quality metrics like freshness, volume, and schema changes, helping you stay on top of SLA compliance. Plus, with built-in data governance features and field-level lineage, it ensures transparency and accountability throughout your data ecosystem.

Sifflet is designed to make data quality monitoring scalable and business-aware. It offers automated anomaly detection, real-time alerts, and impact analysis so you can focus on the issues that matter most. With features like data profiling, dynamic thresholding, and low-code setup, Sifflet empowers both technical and non-technical users to maintain high data reliability across complex pipelines. It's a great fit for modern data teams looking to reduce manual effort and improve trust in their data.