Analytics Trust and Reliability
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


Frequently asked questions
How does Sifflet support data pipeline monitoring at Carrefour?
Sifflet enables comprehensive data pipeline monitoring through features like monitoring-as-code and seamless integration with data lineage tracking and governance tools. This gives Carrefour full visibility into their pipeline health and helps ensure SLA compliance.
How is Sifflet using AI to improve data observability?
We're leveraging AI to make data observability smarter and more efficient. Our AI agent automates monitor creation and provides actionable insights for anomaly detection and root cause analysis. It's all about reducing manual effort while boosting data reliability at scale.
Can Sifflet detect unexpected values in categorical fields?
Absolutely. Sifflet’s data quality monitoring automatically flags unforeseen values in categorical fields, which is a common issue for analytics engineers. This helps prevent silent errors in your data pipelines and supports better SLA compliance across your analytics workflows.
How does integrating data observability improve SLA compliance?
Integrating data observability helps you stay on top of data issues before they impact your users. With real-time metrics, pipeline error alerting, and dynamic thresholding, you can catch problems early and ensure your data meets SLA requirements. This proactive monitoring helps teams maintain trust and deliver consistent, high-quality data services.
Can I use Sifflet’s data observability tools with other platforms besides Airbyte?
Absolutely! While we’ve built a powerful solution for Airbyte, our Declarative Lineage API is flexible enough to support other platforms like Kafka, Census, Hightouch, and Talend. You can use our sample Python scripts to integrate lineage from these tools and enhance your overall data observability strategy.
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.
Is Sifflet planning to offer native support for Airbyte in the future?
Yes, we're excited to share that a native Airbyte connector is in the works! This will make it even easier to integrate and monitor Airbyte pipelines within our observability platform. Stay tuned as we continue to enhance our capabilities around data lineage, automated root cause analysis, and pipeline resilience.
Can I see how a business metric is calculated in Sifflet?
Absolutely! With Sifflet’s data lineage tracking, users can view the full column-level lineage from ingestion to consumption. This transparency helps users understand how each metric is computed and how it relates to other data or metrics in the pipeline.