Yes, and that’s one of the things we’re most excited about! Sifflet empowers non-technical users to define custom monitoring rules and participate in data quality efforts without needing to write dbt code. It’s all part of building a culture of shared responsibility around data governance and observability.

Logs capture interactions between data and external systems or users, offering valuable insights into data transformations and access patterns. They are essential for detecting anomalies, understanding data drift, and improving incident response in both batch and streaming data monitoring environments.

Schema monitoring helps catch unexpected changes in your data structure before they break downstream systems like dashboards or ML models. It's a core capability in any modern observability platform because it ensures data reliability and prevents silent failures in your pipelines.

Agentic observability is our vision for the future — where observability platforms don’t just monitor, they act. Think of it as moving from real-time alerts to intelligent copilots. With features like auto-remediation, dynamic thresholding, and incident response automation, Sifflet is building systems that can detect issues, assess impact, and even resolve known problems on their own. It’s a huge step toward self-healing pipelines and truly proactive data operations.

When choosing a reverse ETL tool, key features to consider include reliable syncing, strong security and privacy controls, and broad integration capabilities. These features help ensure smooth data pipeline monitoring and support data governance across your organization.

Great question! Infrastructure tools like Datadog are excellent at monitoring system uptime, server health, and network performance, but they lack visibility into the actual content of your data. That means they can’t catch silent data issues like null values or schema changes that break downstream dashboards. That’s where a data observability platform like Sifflet comes in—it ensures your data is accurate, complete, and trustworthy, not just delivered on time.

Absolutely! Flow Stopper supports integration with popular tools like Airflow for orchestration and Snowflake for storage. It can run anomaly detection and data validation rules mid-pipeline, helping ensure data quality as it moves through your stack.

Yes! Sifflet now offers an officially-supported Terraform provider that allows you to manage your observability setup as code. This includes configuring monitors and other Sifflet objects, which helps enforce data contracts, improve reproducibility, and strengthen data governance.