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.

As Shippeo scaled, they faced silent data leaks, inconsistent metrics, and data quality issues that impacted billing and reporting. By adopting Sifflet, they gained visibility into their data pipelines and could proactively detect and fix problems before they reached end users.

CTEs help simplify complex queries by breaking them into manageable parts. This boosts readability and performance, making it easier to identify issues during root cause analysis and enhancing your data quality monitoring efforts.

Great question! While data quality monitoring focuses on alerting teams when data deviates from expected parameters, data observability goes further by providing context through data lineage tracking, real-time metrics, and root cause analysis. This holistic view helps teams not only detect issues but also understand and fix them faster, making it a more proactive approach.

Anomaly detection helps identify unexpected changes in data that might signal contract violations or semantic drift. By integrating predictive analytics monitoring and dynamic thresholding into your observability platform, you can catch issues before they break dashboards or compromise AI models. It’s a core feature of a resilient, intelligent metadata layer.

Absolutely. AI systems need more than just schemas—they need context. Observability platforms like Sifflet provide machine-readable trust signals, data freshness checks, and reliability scores through APIs. This allows autonomous agents to assess data quality in real time and make smarter decisions without relying on outdated documentation.

Freshness issues often start with delays in source systems, ingestion bottlenecks, slow transformation jobs, or even caching problems in dashboards. That's why a strong observability platform needs to monitor every stage of the pipeline, from ingestion latency to delivery, to ensure data reliability and timely decision-making.

Great question! Data observability is essential because it gives teams full visibility into the health of their data pipelines. Without it, small issues can quickly snowball into major incidents, like broken dashboards or faulty machine learning models. At Sifflet, we help you catch problems early with real-time metrics and proactive monitoring, so your team can focus on creating insights, not putting out fires.