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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

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Frequently asked questions

Can Sifflet help me trace how data moves through my pipelines?

Absolutely! Sifflet’s data lineage tracking gives you a clear view of how data flows and transforms across your systems. This level of transparency is crucial for root cause analysis and ensuring data governance standards are met.

How does Sifflet support real-time metrics and proactive monitoring?

Sifflet’s observability platform is designed to provide real-time metrics and proactive monitoring through advanced data quality checks, anomaly detection, and custom health scores. This helps data teams catch issues before they escalate, ensuring your data products stay healthy and consistent.

How does Sifflet support proactive data pipeline monitoring?

Sifflet’s observability platform offers proactive data pipeline monitoring through extensive monitoring tools, real-time alerts, and historical performance insights. These features help your team stay ahead of issues and ensure your data pipelines are always delivering high-quality, reliable data.

What is data governance and why does it matter for modern businesses?

Data governance is a framework of policies, roles, and processes that ensure data is accurate, secure, and used responsibly across an organization. It brings clarity and accountability to data management, helping teams trust the data they use, stay compliant with regulations, and make confident decisions. When paired with data observability tools, governance ensures data remains reliable and actionable at scale.

How can I track the success of my data team?

Define clear success KPIs that support ROI, such as improvements in SLA compliance, reduction in ingestion latency, or increased data reliability. Using data observability dashboards and pipeline health metrics can help you monitor progress and communicate value to stakeholders. It's also important to set expectations early and maintain strong internal communication.

How does Sentinel help with data pipeline monitoring?

Sentinel is our monitoring agent that automatically recommends the right monitors based on your data’s structure and usage. By analyzing data samples, column patterns, and relationships, it helps teams scale data pipeline monitoring across hundreds of tables without drowning in alerts. It’s a smarter way to ensure data reliability without manual setup.

Why is semantic quality monitoring important for AI applications?

Semantic quality monitoring ensures that the data feeding into your AI models is contextually accurate and production-ready. At Sifflet, we're making this process seamless with tools that check for data drift, validate schema, and maintain high data quality without manual intervention.

What role does real-time data play in modern analytics pipelines?

Real-time data is becoming a game-changer for analytics, especially in use cases like fraud detection and personalized recommendations. Streaming data monitoring and real-time metrics collection are essential to harness this data effectively, ensuring that insights are both timely and actionable.

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