Sifflet’s unified data catalog and observability features bring context-rich insights into your data workflows. This integration enhances data lineage tracking and supports stronger data governance by giving teams a holistic view of how data flows and transforms across your systems.

Absolutely! SQL Table Tracer is designed with a minimal API and modular architecture, making it easy to plug into larger observability platforms. It provides the foundational data needed for building features like data lineage tracking, pipeline health dashboards, and SLA monitoring.

A strong observability platform should offer data lineage tracking, real-time metrics, anomaly detection, and data freshness checks. It should also integrate with your existing tools like Airflow or Snowflake, and support alerting through Slack or webhook integrations. These capabilities help teams monitor data pipelines effectively and respond quickly to issues.

We’re just getting started! Soon, you’ll be able to monitor dbt run performance and resource utilization, define monitors in your dbt YAML files, and use custom metadata even more dynamically. These updates will further enhance your cloud data observability and make your workflows even more efficient.

We’ve got an exciting lineup! Join us for talks on building trust through data observability, monitoring and tracing data assets at scale, and transforming data skepticism into collaboration. Don’t miss our session on how to unlock the power of data observability for your organization.

Passive metadata is the backbone of Sifflet's observability platform. It fuels the data catalog, supports anomaly detection, and enables tools like Sentinel and Sage to monitor data quality, trace issues, and automate responses. Without passive metadata, real-time metrics and lineage insights wouldn’t be possible.

ETL (Extract, Transform, Load) transforms data before storing it, while ELT (Extract, Load, Transform) loads raw data first, then transforms it. With modern cloud storage, ELT is often preferred for its flexibility and scalability. Whichever method you choose, pairing it with strong data pipeline monitoring ensures smooth operations.

Data observability is the ability to monitor and understand the health of your data across the entire data stack. As data pipelines become more complex, having real-time visibility into where and why data issues occur helps teams maintain data reliability and trust. At Sifflet, we believe data observability is essential for proactive data quality monitoring and faster root cause analysis.