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Frequently asked questions

How does data observability support data governance and compliance?
If you're in a regulated industry or handling sensitive data, observability tools can help you stay compliant. They offer features like audit logging, data freshness checks, and schema validation, which support strong data governance and help ensure SLA compliance.
What makes Sifflet’s approach to anomaly detection more reliable than traditional methods?
Sifflet uses intelligent, ML-driven anomaly detection that evolves with your data. Instead of relying on static rules, it adjusts sensitivity and parameters in real time, improving data reliability and helping teams focus on real issues without being overwhelmed by alert fatigue.
What is a Single Source of Truth, and why is it so hard to achieve?
A Single Source of Truth (SSOT) is a centralized repository where all organizational data is stored and accessed consistently. While it sounds ideal, achieving it is tough because different tools often measure data in unique ways, leading to multiple interpretations. Ensuring data reliability and consistency across sources is where data observability platforms like Sifflet can make a real difference.
Who should be the first hire on a new data team?
If you're just starting out, look for someone with 'Full Data Stack' capabilities, like a Data Analyst with strong SQL and business acumen or a Data Engineer with analytics skills. This person can work closely with other teams to build initial pipelines and help shape your data platform. As your needs evolve, you can grow your team with more specialized roles.
Can I monitor my BigQuery data with Sifflet?
Absolutely! Sifflet’s observability tools are fully compatible with Google BigQuery, so you can perform data quality monitoring, data lineage tracking, and anomaly detection right where your data lives.
Can I add non-integrated tools like Salesforce or HubSpot to my data catalog?
Absolutely! With Sifflet’s declarative framework, you can programmatically declare assets from tools like Salesforce, SAP, or HubSpot, even if they aren’t natively integrated. This helps you maintain a complete and unified view of your data ecosystem for better data governance.
Can better design really improve data reliability and efficiency?
Absolutely. A well-designed observability platform not only looks good but also enhances user efficiency and reduces errors. By streamlining workflows for tasks like root cause analysis and data drift detection, Sifflet helps teams maintain high data reliability while saving time and reducing cognitive load.
What’s the difference between static and dynamic freshness monitoring modes?
Great question! In static mode, Sifflet checks whether data has arrived during a specific time slot and alerts you if it hasn’t. In dynamic mode, our system learns your data arrival patterns over time and only sends alerts when something truly unexpected happens. This helps reduce alert fatigue while maintaining high standards for data quality monitoring.
Still have questions?