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Frequently asked questions

What’s next for Sifflet’s metrics observability capabilities?
We’re expanding support to more BI and transformation tools beyond Looker, and enhancing our ML-based monitoring to group business metrics by domain. This will improve consistency and make it even easier for users to explore metrics across the semantic layer.
Can Sifflet help with root cause analysis when data issues arise?
Absolutely! Sifflet’s field-level data lineage tracking lets you trace data issues from BI dashboards all the way back to source systems. Its AI agent, Sage, even recalls past incidents to suggest likely causes, making root cause analysis faster and more accurate for data engineers and analysts alike.
What role does Sifflet’s Data Catalog play in data governance?
Sifflet’s Data Catalog supports data governance by surfacing labels and tags, enabling classification of data assets, and linking business glossary terms for standardized definitions. This structured approach helps maintain compliance, manage costs, and ensure sensitive data is handled responsibly.
How does Etam ensure pipeline health while scaling its data operations?
Etam uses observability tools like Sifflet to maintain a healthy data pipeline. By continuously monitoring real-time metrics and setting up proactive alerts, they can catch issues early and ensure their data remains trustworthy as they scale operations.
What trends in data observability should we watch for in 2025?
In 2025, expect to see more focus on AI-driven anomaly detection, dynamic thresholding, and predictive analytics monitoring. Staying ahead means experimenting with new observability tools, engaging with peers, and continuously aligning your data strategy with evolving business needs.
How does Sifflet help with analytics tools like Looker?
Sifflet extends its end-to-end data observability to Looker, helping you ensure the data powering your dashboards is accurate and reliable. This means fewer surprises and more confidence in your business insights.
What is data lineage and why is it important for data teams?
Data lineage is a visual map that shows how data flows from its source through transformations to its final destination, like dashboards or ML models. It's essential for data teams because it enables faster root cause analysis, improves data trust, and supports smarter change management. When paired with a data observability platform like Sifflet, lineage becomes a powerful tool for tracking data quality and ensuring SLA compliance.
How did Sifflet help reduce onboarding time for new data team members at jobvalley?
Sifflet’s data catalog provided a clear and organized view of jobvalley’s data assets, making it much easier for new team members to understand the data landscape. This significantly cut down onboarding time and helped new hires become productive faster.
Still have questions?