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
What are the main challenges of implementing Data as a Product?
Some key challenges include ensuring data privacy and security, maintaining strong data governance, and investing in data optimization. These areas require robust monitoring and compliance tools. Leveraging an observability platform can help address these issues by providing visibility into data lineage, quality, and pipeline performance.
What is data observability, and why is it important for companies like Hypebeast?
Data observability is the ability to understand the health, reliability, and quality of data across your ecosystem. For a data-driven company like Hypebeast, it helps ensure that insights are accurate and trustworthy, enabling better decision-making across teams.
Why is data observability important during cloud migration?
Great question! Data observability helps you monitor the health and integrity of your data as it moves to the cloud. By using an observability platform, you can track data lineage, detect anomalies, and validate consistency between environments, which reduces the risk of disruptions and broken pipelines.
Is Sifflet planning to offer native support for Airbyte in the future?
Yes, we're excited to share that a native Airbyte connector is in the works! This will make it even easier to integrate and monitor Airbyte pipelines within our observability platform. Stay tuned as we continue to enhance our capabilities around data lineage, automated root cause analysis, and pipeline resilience.
How did Sifflet help Meero reduce the time spent on troubleshooting data issues?
Sifflet significantly cut down Meero's troubleshooting time by enabling faster root cause analysis. With real-time alerts and automated anomaly detection, the data team was able to identify and resolve issues in minutes instead of hours, saving up to 50% of their time.
Can I deploy Sifflet in my own environment for better control?
Absolutely! Sifflet offers both SaaS and self-managed deployment models. With the self-managed option, you can run the platform entirely within your own infrastructure, giving you full control and helping meet strict compliance and security requirements.
What are some engineering challenges around the 'right to be forgotten' under GDPR?
The 'right to be forgotten' introduces several technical hurdles. For example, deleting user data across multiple systems, backups, and caches can be tricky. That's where data lineage tracking and pipeline orchestration visibility come in handy. They help you understand dependencies and ensure deletions are complete and safe without breaking downstream processes.
How does Sifflet help scale dbt environments without compromising data quality?
Great question! Sifflet enhances your dbt environment by adding a robust data observability layer that enforces standards, monitors key metrics, and ensures data quality monitoring across thousands of models. With centralized metadata, automated monitors, and lineage tracking, Sifflet helps teams avoid the usual pitfalls of scaling like ownership ambiguity and technical debt.