At Sifflet,
Data Means Business.

Great question! In static mode, Sifflet checks whether data has arrived during a specific time slot and alerts you if it hasn’t. In dynamic mode, our system learns your data arrival patterns over time and only sends alerts when something truly unexpected happens. This helps reduce alert fatigue while maintaining high standards for data quality monitoring.

Great question! Sifflet's distribution deviation monitoring uses advanced statistical models to detect shifts in data at the field level. This helps machine learning engineers stay ahead of data drift, maintain model accuracy, and ensure reliable predictive analytics monitoring over time.

Data lineage tracking is key to understanding how data flows through your systems. It helps teams visualize the origin and transformation of datasets, making root cause analysis and impact assessments much faster. For teams focused on data observability and pipeline health, this feature is a must-have.

Our Head of Product, Martin Zerbib, will walk you through how Sifflet enables data lineage tracking, real-time metrics, and data profiling at scale. You’ll get a sneak peek at our roadmap and see how we’re making data more accessible and reliable for teams of all sizes.

Full Data Stack Observability means having complete visibility into every layer of your data pipeline, from ingestion to business intelligence tools. At Sifflet, our observability platform collects signals across your entire stack, enabling anomaly detection, data lineage tracking, and real-time metrics collection. This approach helps teams ensure data reliability and reduce time spent firefighting issues.

Absolutely! One of our goals is to democratize data observability. The Sifflet AI Assistant is designed to be accessible to both technical and non-technical users, offering natural language interfaces and actionable insights that simplify data quality monitoring across the organization.

With more teams depending on data for everyday decisions, data reliability has become a top priority. It’s not just about infrastructure uptime anymore, but also about ensuring the data itself is accurate, fresh, and trustworthy. Tools for data quality monitoring and root cause analysis help teams catch issues early and maintain confidence in their analytics.

Data observability plays a key role in Hypebeast’s MLOps strategy by monitoring data quality from ML models before it reaches dashboards or decision systems. This ensures that AI-driven insights are trustworthy and aligned with business goals.