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.

Great question! Metadata adds the context that traditional monitoring lacks. It helps you understand not just what failed, but also where, why, and who owns it. By layering in technical, operational, and business metadata, your data monitoring becomes smarter and more actionable—making it easier to maintain data quality and reliability across your stack.

The 'Metadata Ceiling' refers to the limitations of infrastructure-first observability tools like Datadog when it comes to understanding the actual content and business impact of data. While Datadog excels at monitoring pipeline health and system performance, it lacks the deep data observability features required to catch issues like null values in critical reports or corrupted inputs in AI models. For full visibility into data quality and business relevance, a specialized observability platform like Sifflet is often a better fit.

You can measure the ROI of a data observability platform by tracking key metrics like the number of data incidents per year, time to detection, and time to resolution. These real-time metrics give you insight into how often issues occur and how quickly your team can resolve them. Don’t forget to factor in qualitative benefits too, like improved team satisfaction and stronger data governance.

Treating data as a product means managing data with the same care and strategy as a traditional product. It involves packaging, maintaining, and delivering high-quality data that serves a specific purpose or audience. This approach improves data reliability and makes it easier to monetize or use for strategic decision-making.

Sifflet's data catalog is enriched with metadata, schema versions, usage stats, and even health status indicators. This makes it easy for users to search, filter, and understand data assets in context. Plus, it integrates seamlessly with your data sources, so you always have the most up-to-date view of your data ecosystem.

Sifflet is built to scale. It supports automated data quality monitoring across hundreds of assets, as seen with Carrefour Links monitoring over 800 data assets in 8+ countries. With dynamic thresholding, schema change detection, and real-time metrics, Sifflet ensures SLA compliance and consistent data reliability across complex ecosystems.

Both Monte Carlo and Acceldata offer root cause analysis, but they focus on different layers. Monte Carlo excels at field-level lineage and visualizing what changed in your data, while Acceldata digs into infrastructure-level issues like Kafka failures or resource limits. Depending on your needs, either can be a powerful observability tool.