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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

What kind of visibility does Sifflet provide for Airflow DAGs?

Sifflet offers a clear view of DAG run statuses and their potential impact on the rest of your data pipeline. Combined with data lineage tracking, it gives you full transparency, making root cause analysis and incident response much easier.

What’s the difference between batch ingestion and real-time ingestion?

Batch ingestion processes data in chunks at scheduled intervals, making it ideal for non-urgent tasks like overnight reporting. Real-time ingestion, on the other hand, handles streaming data as it arrives, which is perfect for use cases like fraud detection or live dashboards. If you're focused on streaming data monitoring or real-time alerts, real-time ingestion is the way to go.

How can I monitor the health of my ingestion pipelines?

To keep your ingestion pipelines healthy, it's best to use observability tools that offer features like pipeline health dashboards, data quality monitoring, and anomaly detection. These tools provide visibility into data flow, alert you to schema drift, and help with root cause analysis when issues arise.

What benefits can I expect from using Sifflet with Google Cloud?

By combining Sifflet with Google Cloud, you get end-to-end cloud data observability, real-time metrics, and proactive monitoring across your data stack. It’s a powerful way to boost your data reliability and meet your SLA compliance goals.

How does Kubernetes help with container orchestration?

Kubernetes makes it easier to manage large-scale containerized applications by automating deployment, scaling, and operations. It's a powerful observability tool that supports real-time metrics collection, resource utilization tracking, and pipeline orchestration visibility, helping teams stay on top of their data pipelines.

What role does MCP play in improving data quality monitoring?

MCP enables LLMs to access structured context like schema changes, validation rules, and logs, making it easier to detect and explain data quality issues. With tool calls and memory, agents can continuously monitor pipelines and proactively alert teams when data quality deteriorates. This supports better SLA compliance and more reliable data operations.

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.

Why is data observability important for business outcomes?

Data observability helps align technical metrics with strategic business goals. By monitoring real-time metrics and enabling root cause analysis, teams can quickly detect and resolve data issues, reducing downtime and improving decision-making. It’s not just about the data, it’s about the impact that data has on your business.

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