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Get ahead of business issues before they become business catastrophes.

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

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’s observability platform help reduce alert fatigue?

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.

What is the MCP Server and how does it help with data observability?

The MCP (Model Context Protocol) Server is a new interface that lets you interact with Sifflet directly from your development environment. It's designed to make data observability more seamless by allowing you to query assets, review incidents, and trace data lineage without leaving your IDE or notebook. This helps streamline your workflow and gives you real-time visibility into pipeline health and data quality.

What makes Sifflet different from Datadog when it comes to root cause analysis?

While Datadog excels at system triage by identifying infrastructure failures, Sifflet focuses on data forensics. Our platform uses root cause analysis to trace data anomalies back to their origin, whether it's a faulty dbt job or a schema change. This kind of insight is crucial for data teams who need to understand why the data is wrong, not just whether the pipeline ran successfully.

What are some signs that our organization might need better data observability?

If your team struggles with delayed dashboards, inconsistent metrics, or unclear data lineage, it's likely time to invest in a data observability solution. At Sifflet, we even created a simple diagnostic to help you assess your data temperature. Whether you're in a 'slow burn' or a 'five alarm fire' state, we can help you improve data reliability and pipeline health.

Why is metadata observability so important in an Open Data Stack?

In an Open Data Stack, metadata acts as the new control plane, guiding how different engines interpret and interact with your data. Without active metadata observability, you're at risk of schema drift, catalog mismatches, and invisible data errors. Sifflet helps you stay ahead by continuously monitoring metadata changes and ensuring data reliability across your stack.

What’s the difference between static and dynamic freshness monitoring modes?

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.

What role does data pipeline monitoring play in Dailymotion’s delivery optimization?

By rebuilding their pipelines with strong data pipeline monitoring, Dailymotion reduced storage costs, improved performance, and ensured consistent access to delivery data. This helped eliminate data sprawl and created a single source of truth for operational teams.

Can I build custom observability dashboards using Sifflet data?

Absolutely! With Sifflet's Data Sharing, you can connect your favorite BI tools like Looker, Tableau, or Power BI to our shared tables. This lets you build tailored dashboards and reports using real-time metrics from your observability data, helping you track KPIs, monitor SLA compliance, and visualize trends across teams or domains.

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