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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

Why is agentic observability critical for modern data environments?

Modern data environments are complex, distributed, and constantly evolving. Agentic observability is essential because it brings AI-powered automation to the forefront, enabling proactive monitoring, anomaly detection, and dynamic thresholding. It’s a scalable approach to managing data drift detection, pipeline health, and incident response in real time.

How does data ingestion relate to data observability?

Great question! Data ingestion is where observability starts. Once data enters your system, observability platforms like Sifflet help monitor its quality, detect anomalies, and ensure data freshness. This allows teams to catch ingestion issues early, maintain SLA compliance, and build trust in their data pipelines.

What role does data lineage tracking play in volume monitoring?

Data lineage tracking is essential for root cause analysis when volume anomalies occur. It helps you trace where data came from and how it's been transformed, so if a volume drop happens, you can quickly identify whether it was caused by a failed API, upstream filter, or schema change. This context is key for effective data pipeline monitoring.

Why is data observability gaining momentum now, even though software observability has been around for a while?

Great question! Software observability took off in the 2010s with the rise of cloud-native apps, but data observability is catching up fast. As businesses start treating data as a mission-critical asset—especially with the growth of AI and cloud data platforms like Snowflake—the need for real-time visibility, data reliability, and governance has become urgent. We're in the early innings, but the pace is accelerating quickly.

What makes Sifflet’s Data Catalog different from built-in catalogs like Snowsight or Unity Catalog?

Unlike tool-specific catalogs, Sifflet serves as a 'Catalog of Catalogs.' It brings together metadata from across your entire data ecosystem, providing a single source of truth for data lineage tracking, asset discovery, and SLA compliance.

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.

What future observability goals has Carrefour set?

Looking ahead, Carrefour plans to expand monitoring to more than 1,500 tables, integrate AI-driven anomaly detection, and implement data contracts and SLA monitoring to further strengthen data governance and accountability.

What’s new in Sifflet’s data quality monitoring capabilities?

We’ve rolled out several powerful updates to help you monitor data quality more effectively. One highlight is our new referential integrity monitor, which ensures logical consistency between tables, like verifying that every order has a valid customer ID. We’ve also enhanced our Data Quality as Code framework, making it easier to scale monitor creation with templates and for-loops.

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