Great question! Data ingestion is where observability starts. Once data enters your system, observability platforms like Sifflet help monitor its quality, detect anomalies, and ensure data freshness. This allows teams to catch ingestion issues early, maintain SLA compliance, and build trust in their data pipelines.

SQL Table Tracer (STT) is a lightweight library that extracts table-level lineage from SQL queries. It plays a key role in data observability by identifying upstream and downstream tables, making it easier to understand data dependencies and track changes across your data pipelines.

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.

With data observability, teams get real-time metrics and deep context around data issues. This means faster incident response and better SLA compliance. Sifflet’s observability platform helps you pinpoint root causes quickly, reducing downtime and giving stakeholders confidence in the reliability of your data.

At Sifflet, we consider data lineage a core part of Full Data Stack Observability because it connects data quality monitoring with data discovery. By mapping data dependencies, teams can detect anomalies faster, perform accurate root cause analysis, and maintain trust in their data pipelines.

We're excited to use this funding to accelerate product innovation, expand our North American presence, and grow our team. Our focus will be on enhancing AI-powered capabilities, improving data pipeline monitoring, and helping customers maintain data reliability at scale.

Yes, onboarding is seamless. You can quickly integrate Sifflet Insights with your existing BI tools and start receiving real-time metrics and alerts. It’s designed to enhance efficiency and support incident response automation without disrupting your current workflows.

Great question! The old playbook for data observability focused heavily on technical infrastructure and treated data like servers — if the pipeline ran and the schema looked fine, the data was assumed to be trustworthy. But today, data is a strategic asset that powers business decisions, AI models, and customer experiences. At Sifflet, we believe modern observability platforms must go beyond uptime and freshness checks to provide context-aware insights that reflect real business impact.