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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

How do declared assets improve data quality monitoring?

Declared assets appear in your Data Catalog just like built-in assets, with full metadata and business context. This improves data quality monitoring by making it easier to track data lineage, perform data freshness checks, and ensure SLA compliance across your entire pipeline.

What is dbt Impact Analysis and how does it help with data observability?

dbt Impact Analysis is a new feature from Sifflet that automatically comments on GitHub or GitLab pull requests with a list of impacted assets when a dbt model is changed. This helps teams enhance their data observability by understanding downstream effects before changes go live.

How does Sifflet support data documentation in Airflow?

Sifflet centralizes documentation for all your data assets, including DAGs, models, and dashboards. This makes it easier for teams to search, explore dependencies, and maintain strong data governance practices.

How does automated data lineage improve data reliability?

Automated data lineage boosts data reliability by giving teams a clear, real-time view of data flows and dependencies. This visibility supports faster troubleshooting, better data governance, and improved SLA compliance, especially when combined with other observability tools in your stack.

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 is data lineage and why does it matter for modern data teams?

Data lineage is the process of mapping the journey of data from its origin to its final destination, including all the transformations it undergoes. It's essential for data pipeline monitoring and root cause analysis because it helps teams quickly identify where data issues originate, saving time and reducing stress under pressure.

Can Sifflet integrate with my existing data stack for seamless data pipeline monitoring?

Absolutely! One of Sifflet’s strengths is its seamless integration across your existing data stack. Whether you're working with tools like Airflow, Snowflake, or Kafka, Sifflet helps you monitor your data pipelines without needing to overhaul your infrastructure.

How does the rise of unstructured data impact data quality monitoring?

Unstructured data, like text, images, and audio, is growing rapidly due to AI adoption and IoT expansion. This makes data quality monitoring more complex but also more essential. Tools that can profile and validate unstructured data are key to maintaining high-quality datasets for both traditional and AI-driven applications.

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