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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 does a data catalog improve data reliability and governance?

A well-managed data catalog enhances data reliability by capturing metadata like data lineage, ownership, and quality indicators. It supports data governance by enforcing access controls and documenting compliance requirements, making it easier to meet regulatory standards and ensure trustworthy analytics across the organization.

Why is a centralized Data Catalog important for data reliability and SLA compliance?

A centralized Data Catalog like Sifflet’s plays a key role in ensuring data reliability and SLA compliance by offering visibility into asset health, surfacing incident alerts, and providing real-time metrics. This empowers teams to monitor data pipelines proactively and meet service level expectations more consistently.

Why is data observability becoming more important than just monitoring?

As data systems grow more complex with cloud infrastructure and distributed pipelines, simple monitoring isn't enough. Data observability platforms like Sifflet go further by offering data lineage tracking, anomaly detection, and root cause analysis. This helps teams not just detect issues, but truly understand and resolve them faster—saving time and avoiding costly outages.

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.

How do Service Level Indicators (SLIs) help improve data product reliability?

SLIs are a fantastic way to measure the health and performance of your data products. By tracking metrics like data freshness, anomaly detection, and real-time alerts, you can ensure your data meets expectations and stays aligned with your team’s SLA compliance goals.

Can I monitor my BigQuery data with Sifflet?

Absolutely! Sifflet’s observability tools are fully compatible with Google BigQuery, so you can perform data quality monitoring, data lineage tracking, and anomaly detection right where your data lives.

Is Sifflet suitable for non-technical users who want to contribute to data quality?

Yes, and that’s one of the things we’re most excited about! Sifflet empowers non-technical users to define custom monitoring rules and participate in data quality efforts without needing to write dbt code. It’s all part of building a culture of shared responsibility around data governance and observability.

Who should be responsible for data quality in an organization?

That's a great topic! While there's no one-size-fits-all answer, the best data quality programs are collaborative. Everyone from data engineers to business users should play a role. Some organizations adopt data contracts or a Data Mesh approach, while others use centralized observability tools to enforce data validation rules and ensure SLA compliance.

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