Shared Understanding. Ultimate Confidence. At Scale.

When everyone knows your data is systematically validated for quality, understands where it comes from and how it's transformed, and is aligned on freshness and SLAs, what’s not to trust?

Always Fresh. Always Validated.

No more explaining data discrepancies to the C-suite. Thanks to automatic and systematic validation, Sifflet ensures your data is always fresh and meets your quality requirements. Stakeholders know when data might be stale or interrupted, so they can make decisions with timely, accurate data.

  • Automatically detect schema changes, null values, duplicates, or unexpected patterns that could comprise analysis.
  • Set and monitor service-level agreements (SLAs) for critical data assets.
  • Track when data was last updated and whether it meets freshness requirements

Understand Your Data, Inside and Out

Give data analysts and business users ultimate clarity. Sifflet helps teams understand their data across its whole lifecycle, and gives full context like business definitions, known limitations, and update frequencies, so everyone works from the same assumptions.

  • Create transparency by helping users understand data pipelines, so they always know where data comes from and how it’s transformed. 
  • Develop shared understanding in data that prevents misinterpretation and builds confidence in analytics outputs.
  • Quickly assess which downstream reports and dashboards are affected

Sifflet’s AI Helps Us Focus on What Moves the Business

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

"Enabler of Cross Platform Data Storytelling"

"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

"Building Harmony Between Data and Business With Sifflet"

"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

" Sifflet empowers our teams through Centralized Data Visibility"

"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 allows us to find and trust our data"

"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

"A core component of our data strategy and transformation"

"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 role does data quality monitoring play in a successful data management strategy?
Data quality monitoring is essential for maintaining the integrity of your data assets. It helps catch issues like missing values, inconsistencies, and outdated information before they impact business decisions. Combined with data observability, it ensures that your data catalog reflects trustworthy, high-quality data across the pipeline.
Why is data observability essential for building trusted data products?
Great question! Data observability is key because it helps ensure your data is reliable, transparent, and consistent. When you proactively monitor your data with an observability platform like Sifflet, you can catch issues early, maintain trust with your data consumers, and keep your data products running smoothly.
What is data observability, and why is it important for companies like Hypebeast?
Data observability is the ability to understand the health, reliability, and quality of data across your ecosystem. For a data-driven company like Hypebeast, it helps ensure that insights are accurate and trustworthy, enabling better decision-making across teams.
Can I deploy Sifflet in my own environment for better control?
Absolutely! Sifflet offers both SaaS and self-managed deployment models. With the self-managed option, you can run the platform entirely within your own infrastructure, giving you full control and helping meet strict compliance and security requirements.
What kind of metadata can I see for a Fivetran connector in Sifflet?
When you click on a Fivetran connector node in the lineage, you’ll see key metadata like source and destination, sync frequency, current status, and the timestamp of the latest sync. This complements Sifflet’s existing metadata like owner and last refresh for complete context.
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.
Can container-based environments improve incident response for data teams?
Absolutely. Containerized environments paired with observability tools like Kubernetes and Prometheus for data enable faster incident detection and response. Features like real-time alerts, dynamic thresholding, and on-call management workflows make it easier to maintain healthy pipelines and reduce downtime.
How does Sifflet support collaboration across data teams?
Sifflet promotes un-siloed data quality by offering a unified platform where data engineers, analysts, and business users can collaborate. Features like pipeline health dashboards, data lineage tracking, and automated incident reports help teams stay aligned and respond quickly to issues.