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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 should I consider when choosing a modern observability tool for my data stack?

When evaluating observability tools, consider factors like ease of setup, support for real-time metrics, data freshness checks, and integration with your existing stack. Look for platforms that offer strong data pipeline monitoring, business context in alerts, and cost transparency. Tools like Sifflet also provide fast time-to-value and support for both batch and streaming data observability.

What role do tools like Apache Spark and dbt play in data transformation?

Apache Spark and dbt are powerful tools for managing different aspects of data transformation. Spark is great for large-scale, distributed processing, especially when working with complex transformations and high data volumes. dbt, on the other hand, brings software engineering best practices to SQL-based transformations, making it ideal for analytics engineering. Both tools benefit from integration with observability platforms to ensure transformation pipelines run smoothly and reliably.

What trends are driving the demand for centralized data observability platforms?

The growing complexity of data products, especially with AI and real-time use cases, is driving the need for centralized data observability platforms. These platforms support proactive monitoring, root cause analysis, and incident response automation, making it easier for teams to maintain data reliability and optimize resource utilization.

How does Sifflet support data teams in improving data pipeline monitoring?

Sifflet’s observability platform offers powerful features like anomaly detection, pipeline error alerting, and data freshness checks. We help teams stay on top of their data workflows and ensure SLA compliance with minimal friction. Come chat with us at Booth Y640 to learn more!

Why is integration with my existing tools important for observability?

A good observability platform should fit right into your current stack. That means supporting tools like dbt, Airflow, and your cloud infrastructure. Seamless integration ensures better pipeline orchestration visibility and makes it easier to act on data issues without disrupting your workflows.

Why is data observability so important for AI-powered organizations in 2025?

Great question! As AI continues to evolve, the quality and reliability of the data feeding those models becomes even more critical. Data observability ensures that your AI systems are powered by clean, accurate, and up-to-date data. With platforms like Sifflet, organizations can detect issues like data drift, monitor real-time metrics, and maintain data governance, all of which help AI models stay accurate and trustworthy.

Why are retailers turning to data observability to manage inventory better?

Retailers are adopting data observability to gain real-time visibility into inventory across all channels, reduce stock inaccuracies, and avoid costly misalignments between supply and demand. With data observability tools, they can proactively detect issues, monitor data quality, and improve operational efficiency across their data pipelines.

Why is data observability becoming essential for data-driven companies?

As more businesses rely on data to drive decisions, ensuring data reliability is critical. Data observability provides transparency into the health of your data assets and pipelines, helping teams catch issues early, stay compliant with SLAs, and ultimately build trust in their data.

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