Using an observability platform like Sifflet brings several benefits: real-time anomaly detection, proactive incident management, improved SLA compliance, and better data governance. By combining metrics, metadata, and lineage, we help teams move from reactive data quality monitoring to proactive, scalable observability that supports reliable, data-driven decisions.

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.

Data quality monitoring ensures your data is accurate, complete, and consistent. A good data catalog should include profiling and validation tools that help teams assess data quality, which is crucial for maintaining SLA compliance and enabling proactive monitoring.

Sifflet's integration with Apache Airflow brings powerful data observability features directly into your orchestration workflows. It helps data teams monitor DAG run statuses, understand downstream dependencies, and apply data quality monitoring to catch issues early, ensuring data reliability across the stack.

When selecting a data observability tool, consider your data stack, team size, and specific needs like anomaly detection, metrics collection, or schema registry integration. Whether you're looking for open source observability options or a full-featured commercial platform, make sure it supports your ecosystem and scales with your data operations.

We’ve rolled out several powerful updates to help you monitor data quality more effectively. One highlight is our new referential integrity monitor, which ensures logical consistency between tables, like verifying that every order has a valid customer ID. We’ve also enhanced our Data Quality as Code framework, making it easier to scale monitor creation with templates and for-loops.

Join us on January 22nd for a deep dive into Sifflet’s 2024 highlights and a preview of what’s ahead in 2025. We’ll cover innovations in data observability, including real-time metrics, faster incident resolution, and the upcoming Sifflet AI Agent. It’s the perfect way to kick off the year with fresh insights and inspiration!

Sifflet is designed for both technical and non-technical users, offering no-code monitors, natural-language setup, and cross-persona alerts. This means analysts, data scientists, and executives can all engage with data quality monitoring without needing engineering support, making it a truly inclusive observability platform.