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Frequently asked questions

What kind of visibility does Sifflet provide for Airflow DAGs?
Sifflet offers a clear view of DAG run statuses and their potential impact on the rest of your data pipeline. Combined with data lineage tracking, it gives you full transparency, making root cause analysis and incident response much easier.
What is “data-quality-as-code”?

Data-quality-as-code (DQaC) allows you to programmatically define and enforce data quality rules using code. This ensures consistency, scalability, and better integration with CI/CD pipelines. Read more here to find out how to leverage it within Sifflet

Can I see how a business metric is calculated in Sifflet?
Absolutely! With Sifflet’s data lineage tracking, users can view the full column-level lineage from ingestion to consumption. This transparency helps users understand how each metric is computed and how it relates to other data or metrics in the pipeline.
What are some signs that our organization might need better data observability?
If your team struggles with delayed dashboards, inconsistent metrics, or unclear data lineage, it's likely time to invest in a data observability solution. At Sifflet, we even created a simple diagnostic to help you assess your data temperature. Whether you're in a 'slow burn' or a 'five alarm fire' state, we can help you improve data reliability and pipeline health.
How can I monitor the health of my ingestion pipelines?
To keep your ingestion pipelines healthy, it's best to use observability tools that offer features like pipeline health dashboards, data quality monitoring, and anomaly detection. These tools provide visibility into data flow, alert you to schema drift, and help with root cause analysis when issues arise.
Why is combining dbt Core with a data observability platform like Sifflet a smart move?
Combining dbt Core with a data observability platform like Sifflet helps data teams go beyond transformation and into full-stack monitoring. It enables better root cause analysis, reduces time to resolution, and ensures your data products are trustworthy and resilient.
Can data lineage help with regulatory compliance such as GDPR?
Absolutely. Data lineage supports data governance by mapping data flows and access rights, which is essential for compliance with regulations like GDPR. Features like automated PII propagation help teams monitor sensitive data and enforce security observability best practices.
Is Datadog a good fit for teams focused on data reliability and governance?
Datadog is a strong choice for infrastructure and system observability, but it may not be the best fit for teams focused on data reliability and data governance. While it offers some data quality monitoring through Metaplane, it lacks the business context and advanced data lineage tracking needed to ensure trust in your analytics. For those priorities, a dedicated data observability platform like Sifflet is better equipped.
Still have questions?