


Discover more integrations
No items found.
Get in touch CTA Section
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Frequently asked questions
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.
Does Sifflet store any of my company’s data?
No, Sifflet does not store your data. We designed our platform to discard any data previews immediately after display, and we only retain metadata like table and column names. This approach supports GDPR compliance and strengthens your overall data governance strategy.
How does data observability improve the value of a data catalog?
Data observability enhances a data catalog by adding continuous monitoring, data lineage tracking, and real-time alerts. This means organizations can not only find their data but also trust its accuracy, freshness, and consistency. By integrating observability tools, a catalog becomes part of a dynamic system that supports SLA compliance and proactive data governance.
What’s Sifflet’s vision for data observability in 2025?
Our 2025 vision is all about pushing the boundaries of cloud data observability. We're focusing on deeper automation, AI-driven insights, and expanding our observability platform to cover everything from real-time metrics to predictive analytics monitoring. It's about making data operations more resilient, transparent, and scalable.
Why is data lineage important for GDPR compliance?
Data lineage is essential for GDPR because it helps you trace personal data from source to destination. This means you can see where PII is stored, how it flows through your data pipelines, and which reports or applications use it. With this visibility, you can manage deletion requests, audit data usage, and ensure data governance policies are enforced consistently.
What kind of data quality monitoring does Sifflet offer when used with dbt?
When paired with dbt, Sifflet provides robust data quality monitoring by combining dbt test insights with ML-based rules and UI-defined validations. This helps you close test coverage gaps and maintain high data quality throughout your data pipelines.
What should I look for in terms of integrations when choosing a data observability platform?
Great question! When evaluating a data observability platform, it's important to check how well it integrates with your existing data stack. The more integrations it supports, the more visibility you’ll have across your pipelines. This is key to achieving comprehensive data pipeline monitoring and ensuring smooth observability across your entire data ecosystem.
How does data lineage enhance data observability?
Data lineage adds context to data observability by linking alerts to their root cause. For example, if a metric suddenly drops, lineage helps trace it back to a delayed ingestion or schema change. This speeds up incident resolution and strengthens anomaly detection. Platforms like Sifflet combine lineage with real-time metrics and data freshness checks to provide a complete view of pipeline health.