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Frequently asked questions
What kind of monitoring should I set up after migrating to the cloud?
After migration, continuous data quality monitoring is a must. Set up real-time alerts for data freshness checks, schema changes, and ingestion latency. These observability tools help you catch issues early and keep your data pipelines running smoothly.
What exactly is the modern data stack, and why is it so popular now?
The modern data stack is a collection of cloud-native tools that help organizations transform raw data into actionable insights. It's popular because it simplifies data infrastructure, supports scalability, and enables faster, more accessible analytics across teams. With tools like Snowflake, dbt, and Airflow, teams can build robust pipelines while maintaining visibility through data observability platforms like Sifflet.
Is it hard to set up the Sifflet and ServiceNow integration?
Not at all! It only takes a few minutes to get started. Just follow our step-by-step integration guide, and you’ll be ready to connect your data observability alerts directly to ServiceNow in no time.
Can I trust the data I find in the Sifflet Data Catalog?
Absolutely! Thanks to Sifflet’s built-in data quality monitoring, you can view real-time metrics and health checks directly within the Data Catalog. This gives you confidence in the reliability of your data before making any decisions.
How does Sifflet use AI to improve data observability?
At Sifflet, we're integrating advanced AI models into our observability platform to enhance data quality monitoring and anomaly detection. Marie, our Machine Learning Engineer, has been instrumental in building intelligent systems that automatically detect issues across data pipelines, making it easier to maintain data reliability in real time.
Why is data observability so important for AI and analytics initiatives?
Great question! Data observability ensures that the data fueling AI and analytics is reliable, accurate, and fresh. At Sifflet, we see data observability as both a technical and business challenge, which is why our platform focuses on data quality monitoring, anomaly detection, and real-time metrics to help enterprises make confident, data-driven decisions.
What can I expect to learn from Sifflet’s session on cataloging and monitoring data assets?
Our Head of Product, Martin Zerbib, will walk you through how Sifflet enables data lineage tracking, real-time metrics, and data profiling at scale. You’ll get a sneak peek at our roadmap and see how we’re making data more accessible and reliable for teams of all sizes.
What impact did Sifflet have on fostering a data-driven culture at Meero?
Sifflet’s intuitive UI and real-time data observability dashboards empowered even non-technical users at Meero to understand data health. This transparency helped build trust in data and promoted a stronger data-driven culture across the organization.






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