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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
What kind of monitoring should I set up after migrating to the cloud?
After migration, continuous data quality monitoring is a must. Set up real-time alerts for data freshness checks, schema changes, and ingestion latency. These observability tools help you catch issues early and keep your data pipelines running smoothly.
Why should I care about metadata management in my organization?
Great question! Metadata management helps you understand what data you have, where it comes from, and how it’s being used. It’s a critical part of data governance and plays a huge role in improving data discovery, trust, and overall data reliability. With the right metadata strategy, your team can find the right data faster and make better decisions.
Can Sifflet support SLA compliance and data governance goals?
Absolutely! Sifflet supports SLA compliance through proactive data quality monitoring and real-time metrics. Its deep metadata integrations and lineage tracking also help organizations enforce data governance policies and maintain trust across the entire data ecosystem.
How is Sifflet using AI to improve data observability?
We're leveraging AI to make data observability smarter and more efficient. Our AI agent automates monitor creation and provides actionable insights for anomaly detection and root cause analysis. It's all about reducing manual effort while boosting data reliability at scale.
What role does data lineage tracking play in storage observability?
Data lineage tracking is essential for understanding how data flows from storage to dashboards. When something breaks, Sifflet helps you trace it back to the storage layer, whether it's a corrupted file in S3 or a schema drift in MongoDB. This visibility is critical for root cause analysis and ensuring data reliability across your pipelines.
Why is data freshness so important for data reliability?
Great question! Data freshness is a key part of data reliability because decisions are only as good as the data they're based on. If your data is outdated or delayed, it can lead to flawed insights and missed opportunities. That's why data freshness checks are a foundational element of any strong data observability strategy.
What’s next for Sifflet’s metrics observability capabilities?
We’re expanding support to more BI and transformation tools beyond Looker, and enhancing our ML-based monitoring to group business metrics by domain. This will improve consistency and make it even easier for users to explore metrics across the semantic layer.
How does data observability help ensure SLA compliance for data products?
Data observability plays a big role in SLA compliance by continuously monitoring data freshness, quality, and availability. With tools like Sifflet, teams can set alerts and track metrics that align with their SLAs, ensuring data products meet business expectations consistently.
Data Observability is Now
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