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Frequently asked questions
Can Sifflet help me monitor data drift and anomalies beyond what dbt offers?
Absolutely! While dbt is fantastic for defining tests, Sifflet takes it further with advanced data drift detection and anomaly detection. Our platform uses intelligent monitoring templates that adapt to your data’s behavior, so you can spot unexpected changes like missing rows or unusual values without setting manual thresholds.
Can I use the Fivetran integration to monitor data pipeline health?
Absolutely! By surfacing connector statuses and metadata directly in the lineage graph and catalog, Sifflet helps you stay on top of pipeline health and detect issues early. It's a powerful step forward in proactive data pipeline monitoring.
Why is data observability important for large organizations?
Data observability helps organizations ensure data quality, monitor pipelines in real time, and build trust in their data. At Big Data LDN, we’ll share how companies like Penguin Random House use observability tools to improve data governance and drive better decisions.
Why does query formatting matter in modern data operations?
Well-formatted queries are easier to debug, share, and maintain. This aligns with DataOps best practices and supports transparency in data pipelines, which is essential for consistent SLA compliance and proactive monitoring.
Why did jobvalley choose Sifflet over other data catalog vendors?
After evaluating several data catalog vendors, jobvalley selected Sifflet because of its comprehensive features that addressed both data discovery and data quality monitoring. The platform’s ability to streamline onboarding and support real-time metrics made it the ideal choice for their growing data team.
Can Sifflet’s dbt Impact Analysis help with root cause analysis?
Absolutely! By identifying all downstream assets affected by a dbt model change, Sifflet’s Impact Report makes it easier to trace issues back to their source, significantly speeding up root cause analysis and reducing incident resolution time.
How does Sifflet help with data observability during the CI process?
Sifflet integrates directly with your CI pipelines on platforms like GitHub and GitLab to proactively surface issues before code is merged. By analyzing the impact of dbt model changes and running data quality monitors in testing environments, Sifflet ensures data reliability and minimizes production disruptions.
Is data observability relevant for small businesses?
Yes! While smaller organizations may have fewer data pipelines, ensuring data quality and reliability is equally important for making accurate decisions and scaling effectively. What really matters is the data stack maturity and volume of data. Take our test here to find out if you really need data observability.