Cloud migration monitoring
Mitigate disruption and risks
Optimize the management of data assets during each stage of a cloud migration.

Before migration
- Go through an inventory of what needs to be migrated using the Data Catalog
- Identify the most critical assets to prioritize migration efforts based on actual asset usage
- Leverage lineage to identify downstream impact of the migration in order to plan accordingly
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During migration
- Use the Data Catalog to confirm all the data was backed up appropriately
- Ensure the new environment matches the incumbent via dedicated monitors

After migration
- Swiftly document and classify new pipelines thanks to Sifflet AI Assistant
- Define data ownership to improve accountability and simplify maintenance of new data pipelines
- Monitor new pipelines to ensure the robustness of data foundations over time
- Leverage lineage to better understand newly built data flows


Frequently asked questions
How did jobvalley improve data visibility across their teams?
jobvalley enhanced data visibility by implementing Sifflet’s observability platform, which included a powerful data catalog. This centralized hub made it easier for teams to discover and access the data they needed, fostering better collaboration and transparency across departments.
Why is collaboration important in building a successful observability platform?
Collaboration is key to building a robust observability platform. At Sifflet, our teams work cross-functionally to ensure every part of the platform, from data lineage tracking to real-time metrics collection, aligns with business goals. This teamwork helps us deliver a more comprehensive and user-friendly solution.
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.
How does Sifflet support data pipeline monitoring for teams using dbt?
Sifflet gives you end-to-end visibility into your data pipelines, including those built with dbt. With features like pipeline health dashboards, data freshness checks, and telemetry instrumentation, your team can monitor pipeline performance and ensure SLA compliance with confidence.
Can Sifflet support real-time metrics and monitoring for AI pipelines?
Absolutely! While Sifflet’s monitors are typically scheduled, you can run them on demand using our API. This means you can integrate real-time data quality checks into your AI pipelines, ensuring your models are making decisions based on the freshest and most accurate data available. It's a powerful way to keep your AI systems responsive and reliable.
How does Sifflet help reduce alert fatigue in data observability?
Sifflet uses AI-driven context and dynamic thresholding to prioritize alerts based on impact and relevance. Its intelligent alerting system ensures users only get notified when it truly matters, helping reduce alert fatigue and enabling faster, more focused incident response.
How does Sifflet support reverse ETL and operational analytics?
Sifflet enhances reverse ETL workflows by providing data observability dashboards and real-time monitoring. Our platform ensures your data stays fresh, accurate, and actionable by enabling root cause analysis, data lineage tracking, and proactive anomaly detection across your entire pipeline.
Why is data observability more than just monitoring?
Great question! At Sifflet, we believe data observability is about operationalizing trust, not just catching issues. It’s the foundation for reliable data pipelines, helping teams ensure data quality, track lineage, and resolve incidents quickly so business decisions are always based on trustworthy data.