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Show Your Stack Who’s Boss
Unified data observability that packs a three-in-one punch. From data discovery to integrated monitoring and troubleshooting capabilities, you’ll be the one in charge.
Seamlessly connect with all your favorite data tools to centralize insights and unlock the full potential of your data ecosystem.

Join the ranks of happy customers who’ve made Sifflet a G2 leader, trusted for its innovation and impact
Stay ahead of issues with real-time alerts that keep you informed and in control of your data health
Organize, discover, and leverage your data assets effortlessly with a smart, searchable catalog built for modern teams.
Harness the power of AI-driven suggestions to improve efficiency, accuracy, and decision-making across your workflows.

Empower your team with tailored access, enabling secure collaboration that drives smarter decisions.
Frequently asked questions
How does Sifflet support data quality monitoring for large organizations?
Sifflet is built to scale. It supports automated data quality monitoring across hundreds of assets, as seen with Carrefour Links monitoring over 800 data assets in 8+ countries. With dynamic thresholding, schema change detection, and real-time metrics, Sifflet ensures SLA compliance and consistent data reliability across complex ecosystems.
How do I ensure SLA compliance during a cloud migration?
Ensuring SLA compliance means keeping a close eye on metrics like throughput, resource utilization, and error rates. A robust observability platform can help you track these metrics in real time, so you stay within your service level objectives and keep stakeholders confident.
What should I look for when choosing a data observability platform?
Great question! When evaluating a data observability platform, it’s important to focus on real capabilities like root cause analysis, data lineage tracking, and SLA compliance rather than flashy features. Our checklist helps you cut through the noise so you can find a solution that builds trust and scales with your data needs.
Can Sifflet detect anomalies in my data pipelines?
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.
How do logs contribute to observability in data pipelines?
Logs capture interactions between data and external systems or users, offering valuable insights into data transformations and access patterns. They are essential for detecting anomalies, understanding data drift, and improving incident response in both batch and streaming data monitoring environments.
How is Etam using data observability to support its 2025 strategy?
Etam is leveraging data observability as a foundational element of its 2025 data strategy. With Sifflet’s observability platform, the team can monitor data quality, detect issues early, and ensure data reliability, which helps them move faster and with more confidence across the business.
Can Datadog help with root cause analysis during incidents?
Yes, Datadog is excellent for root cause analysis, especially with its Bits AI SRE feature. This AI-powered assistant automatically investigates incidents by analyzing telemetry data like logs, metrics, and traces, then suggests likely causes and next steps. It’s a major boost for incident response automation and helps reduce mean time to resolution (MTTR).
Why is this integration important for data pipeline monitoring?
Bringing Sifflet’s observability tools into Apache Airflow allows for proactive data pipeline monitoring. You get real-time metrics, anomaly detection, and data freshness checks that help you catch issues early and keep your pipelines healthy.
Data Observability is Now
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