The Universal Connector is one of our most exciting 2024 releases. It enables seamless integration across the entire data lifecycle, helping users achieve complete visibility with end-to-end data observability. This means fewer blind spots and a much more holistic view of your data ecosystem.

Yes, it can! Sifflet uses machine learning for anomaly detection, helping you catch unexpected changes in data volume or quality. You can even label anomalies to improve the model's accuracy over time, reducing alert fatigue and improving incident response automation.

We hear this a lot — too many alerts, not enough clarity. At Sifflet, we focus on intelligent alerting by combining metadata, data lineage tracking, and usage patterns to prioritize what really matters. Instead of just flagging that something broke, our platform tells you who’s affected, why it matters, and how to fix it. That means fewer false positives and more actionable insights, helping you cut through the noise and focus on what truly impacts your business.

Accessibility is a core part of our design philosophy. We ensure that key indicators in our observability tools, such as data freshness checks or pipeline health statuses, are communicated using both color and iconography. This approach supports inclusive experiences for users with visual impairments, including color blindness.

Forge doesn’t take action on its own, but it does provide smart, contextual guidance based on past fixes. It helps teams resolve issues faster while keeping you in full control of the resolution process, which is key for maintaining SLA compliance and data quality monitoring.

Data observability is the ability to monitor, understand, and troubleshoot data health across the entire data stack. It's essential for modern data teams because it helps ensure data reliability, improves trust in analytics, and prevents costly issues caused by broken data pipelines or inaccurate dashboards. With the rise of complex infrastructures and real-time data usage, having a strong observability platform in place is no longer optional.

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.

Absolutely! With shared visibility into data flows and transformations, observability platforms foster better communication between data engineers, analysts, and business users. Everyone can see what's happening in the pipeline, which encourages ownership and teamwork around data reliability.