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.

Not all data is created equal, so it's important to focus data quality monitoring efforts on the assets that drive business outcomes. That means identifying key dashboards, critical metrics, and high-impact models, then using tools like pipeline health dashboards and SLA monitoring to keep them reliable and fresh.

Flow Stopper is a powerful feature in Sifflet's observability platform that allows you to pause vulnerable pipelines at the orchestration layer before issues reach production. It helps with proactive data pipeline monitoring by catching anomalies early and preventing downstream damage to your data systems.

Sifflet offers one of the most advanced data lineage tracking capabilities out there. Think of it like a GPS for your data pipelines—it gives you full traceability, helps identify bottlenecks, and supports better pipeline orchestration visibility. It's a game-changer for data governance and optimization.

Great question! AI and ML systems rely on massive volumes of data to make decisions, and any flaw in that data gets amplified at scale. Data reliability ensures that your models are trained and operate on accurate, complete, and timely data. Without it, you risk cascading failures, poor predictions, and even regulatory issues. That’s why data observability is essential to proactively monitor and maintain reliability across your pipelines.

Preventing bad data starts with proactive data quality monitoring. That includes data profiling, defining clear KPIs, assigning ownership, and using observability tools that provide real-time metrics and alerts. Integrating data lineage tracking also helps you quickly identify where issues originate in your data pipelines.

SQL Table Tracer (STT) is a lightweight library that automatically extracts table-level lineage from SQL queries. It identifies both destination and upstream tables, making it easier to understand data dependencies and build reliable data lineage workflows. This is a key component of any effective data observability strategy.

At Sifflet, we provide a full-stack observability platform that gives teams complete visibility into their data pipelines. From data quality monitoring to root cause analysis and real-time anomaly detection, we help organizations ensure their data is accurate, timely, and trustworthy.