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Integrates with your %%modern data stack%%

Sifflet seamlessly integrates into your data sources and preferred tools, and can run on AWS, Google Cloud Platform, and Microsoft Azure.

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The Sifflet team is always working hard on incorporating more integrations into our product. Get in touch if you want us to keep you updated!

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Sifflet’s AI Helps Us Focus on What Moves the Business

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

"Enabler of Cross Platform Data Storytelling"

"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

"Building Harmony Between Data and Business With Sifflet"

"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

" Sifflet empowers our teams through Centralized Data Visibility"

"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 allows us to find and trust our data"

"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

"A core component of our data strategy and transformation"

"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

What metrics should I track to assess the health of AI systems?

To assess AI health, track metrics like Mean Time to Detection (MTTD), Mean Time to Resolution (MTTR), and data freshness checks. These metrics, combined with robust data pipeline monitoring and anomaly scoring, give you a clear view into model performance and governance effectiveness over time.

Can Sifflet integrate with our existing data tools and platforms?

Absolutely! Sifflet is designed to integrate seamlessly with your current stack. We support a wide range of tools including Airflow, Snowflake, AWS Glue, and more. Our goal is to provide complete pipeline orchestration visibility and data freshness checks, all from one intuitive interface.

How does Sifflet enhance data observability compared to traditional monitoring tools?

Sifflet takes data observability to the next level by combining metadata with AI-powered features like automated root cause analysis, anomaly detection, and impact mapping. Unlike basic monitoring tools, our observability platform doesn't just alert you—it explains what happened and guides you toward resolution, helping teams respond faster and with more confidence.

Why are data consumers becoming more involved in observability decisions?

We’re seeing a big shift where data consumers—like analysts and business users—are finally getting a seat at the table. That’s because data observability impacts everyone, not just engineers. When trust in data is operationalized, it boosts confidence across the business and turns data teams into value creators.

Can data observability support better demand forecasting for retailers?

Absolutely. By integrating historical sales, real-time transactions, and external data sources like weather or social trends, data observability platforms enhance forecast accuracy. They use machine learning to evaluate and adjust predictions, helping retailers align inventory with actual consumer demand more effectively.

How does Sifflet help close the observability gap for Airbyte pipelines?

Great question! Sifflet bridges the observability gap for Airbyte by using our Declarative Lineage API and a custom Python script. This allows you to capture complete data lineage from Airbyte and ingest it into Sifflet, giving you full visibility into your pipelines and enabling better root cause analysis 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.

What are Sentinel, Sage, and Forge, and how do they enhance data observability?

Sentinel, Sage, and Forge are Sifflet’s new AI agents designed to supercharge your data observability efforts. Sentinel proactively recommends monitoring strategies, Sage accelerates root cause analysis by remembering system history, and Forge guides your team with actionable fixes. Together, they help teams reduce alert fatigue and improve data reliability at scale.

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