Sifflet’s observability platform offers powerful features like anomaly detection, pipeline error alerting, and data freshness checks. We help teams stay on top of their data workflows and ensure SLA compliance with minimal friction. Come chat with us at Booth Y640 to learn more!

Sifflet’s observability platform offers proactive data pipeline monitoring through extensive monitoring tools, real-time alerts, and historical performance insights. These features help your team stay ahead of issues and ensure your data pipelines are always delivering high-quality, reliable data.

The four pillars—metrics, metadata, data lineage, and logs—work together to give teams full visibility into their data systems. Metrics help with data profiling and freshness checks, metadata enhances data governance, lineage enables root cause analysis, and logs provide insights into data interactions. Together, they support proactive data quality 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.

To manage a data catalog effectively, assign clear ownership through data stewards, enforce consistent naming conventions, and schedule regular metadata reviews. For even more impact, connect it with your observability platform to monitor data quality and lineage in real time, ensuring your catalog stays accurate and actionable.

Yes, we’re hosting an exclusive after-party at our booth on October 15! Come join us for great conversations, a champagne toast, and a chance to connect with data leaders who care about data governance, pipeline health, and building resilient systems.

Data lineage tracking is essential for root cause analysis because it shows exactly how data flows through your pipeline. With tools like Sifflet, teams can trace issues back to their origin in seconds instead of days. This visibility helps engineers quickly identify and fix the 'first wrong turn' in complex environments, like Adaptavist did during their monorepo-to-polyrepo migration.

Look for features like self-service discovery, automated metadata collection, and end-to-end data lineage. Scalability is key too, especially as your data grows. Tools like Sifflet also integrate data observability, so you can monitor data quality and pipeline health while exploring your data assets.