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Frequently asked questions

Can data observability improve collaboration across data teams?
Absolutely! With shared visibility into data flows and transformations, observability platforms foster better communication between data engineers, analysts, and business users. Everyone can see what's happening in the pipeline, which encourages ownership and teamwork around data reliability.
Can I define data quality monitors as code using Sifflet?
Absolutely! With Sifflet's Data-Quality-as-Code (DQaC) v2 framework, you can define and manage thousands of monitors in YAML right from your IDE. This Everything-as-Code approach boosts automation and makes data quality monitoring scalable and developer-friendly.
Why is field-level lineage important in data observability?
Field-level lineage gives you a detailed view into how individual data fields move and transform through your pipelines. This level of granularity is super helpful for root cause analysis and understanding the impact of changes. A platform with strong data lineage tracking helps teams troubleshoot faster and maintain high data quality.
What is metrics observability and why does it matter for business users?
Metrics observability helps business users trust and understand the KPIs they rely on by making it easy to trace how metrics are defined, calculated, and connected to other data assets. With Sifflet’s observability platform, teams can ensure their business metrics are accurate, reliable, and aligned across departments.
How does data observability improve incident response and SLA compliance?
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.
What tools can help me monitor data consistency between old and new environments?
You can use data profiling and anomaly detection tools to compare datasets before and after migration. These features are often built into modern data observability platforms and help you validate that nothing critical was lost or changed during the move.
Can Sifflet detect unexpected values in categorical fields?
Absolutely. Sifflet’s data quality monitoring automatically flags unforeseen values in categorical fields, which is a common issue for analytics engineers. This helps prevent silent errors in your data pipelines and supports better SLA compliance across your analytics workflows.
How does Sifflet support data documentation in Airflow?
Sifflet centralizes documentation for all your data assets, including DAGs, models, and dashboards. This makes it easier for teams to search, explore dependencies, and maintain strong data governance practices.
Still have questions?