Cost-efficient data pipelines
Pinpoint cost inefficiencies and anomalies thanks to full-stack data observability.


Data asset optimization
- Leverage lineage and Data Catalog to pinpoint underutilized assets
- Get alerted on unexpected behaviors in data consumption patterns

Proactive data pipeline management
Proactively prevent pipelines from running in case a data quality anomaly is detected


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Frequently asked questions
How can Sifflet help ensure SLA compliance and prevent bad data from affecting business decisions?
Sifflet helps teams stay on top of SLA compliance with proactive data freshness checks, anomaly detection, and incident tracking. Business users can rely on health indicators and lineage views to verify data quality before making decisions, reducing the risk of costly errors due to unreliable data.
What role did data observability play in Carrefour’s customer engagement strategy?
Data observability was crucial in maintaining high data quality for loyalty programs and marketing campaigns. With real-time metrics and anomaly detection in place, Carrefour was able to improve customer satisfaction and retention through more accurate and timely insights.
How does Sentinel help reduce alert fatigue in modern data environments?
Sentinel intelligently analyzes metadata like data lineage and schema changes to recommend what really needs monitoring. By focusing on high-impact areas, it cuts down on noise and helps teams manage alert fatigue while optimizing monitoring costs.
Why is data observability gaining momentum now, even though software observability has been around for a while?
Great question! Software observability took off in the 2010s with the rise of cloud-native apps, but data observability is catching up fast. As businesses start treating data as a mission-critical asset—especially with the growth of AI and cloud data platforms like Snowflake—the need for real-time visibility, data reliability, and governance has become urgent. We're in the early innings, but the pace is accelerating quickly.
Can I learn about real-world results from Sifflet customers at the event?
Yes, definitely! Companies like Saint-Gobain will be sharing how they’ve used Sifflet for data observability, data lineage tracking, and SLA compliance. It’s a great chance to hear how others are solving real data challenges with our platform.
How does data observability complement a data catalog?
While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.
Is Forge able to automatically fix data issues in my pipelines?
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.
What improvements has Sifflet made to incident management workflows?
We’ve introduced Augmented Resolution to help teams group related alerts into a single collaborative ticket, streamlining incident response. Plus, with integrations into your ticketing systems, Sifflet ensures that data issues are tracked, communicated, and resolved efficiently. It’s all part of our mission to boost data reliability and support your operational intelligence.