Solution by Use Case

Cloud migration monitoring

Mitigate disruption and risks

Optimize the management of data assets during each stage of a cloud migration.

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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

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

Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

" Sifflet empowers our teams through Centralized Data Visibility"

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"A core component of our data strategy and transformation"

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

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Frequently asked questions

Why is the new join feature in the monitor UI a game changer for data quality monitoring?

The ability to define joins directly in the monitor setup interface means you can now monitor relationships across datasets without writing custom SQL. This is crucial for data quality monitoring because many issues arise from inconsistencies between related tables. Now, you can catch those problems early and ensure better data reliability across your pipelines.

Is Forge able to automatically fix data issues in my pipelines?

Forge doesn’t take action on its own, but it does provide smart, contextual guidance based on past fixes. It helps teams resolve issues faster while keeping you in full control of the resolution process, which is key for maintaining SLA compliance and data quality monitoring.

What is data observability and why is it important for modern data teams?

Data observability is the ability to monitor and understand the health of your data across the entire data stack. As data pipelines become more complex, having real-time visibility into where and why data issues occur helps teams maintain data reliability and trust. At Sifflet, we believe data observability is essential for proactive data quality monitoring and faster root cause analysis.

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.

Why is data observability becoming more important than just monitoring?

As data systems grow more complex with cloud infrastructure and distributed pipelines, simple monitoring isn't enough. Data observability platforms like Sifflet go further by offering data lineage tracking, anomaly detection, and root cause analysis. This helps teams not just detect issues, but truly understand and resolve them faster—saving time and avoiding costly outages.

What’s the best way to prevent bad data from impacting our business decisions?

Preventing bad data starts with proactive data quality monitoring. That includes data profiling, defining clear KPIs, assigning ownership, and using observability tools that provide real-time metrics and alerts. Integrating data lineage tracking also helps you quickly identify where issues originate in your data pipelines.

How does Sifflet support AI readiness within enterprises?

Sifflet reinforces AI-powered capabilities through features like data freshness checks, data profiling, and anomaly scoring. These tools ensure your data is accurate and trustworthy, which is crucial for training reliable machine learning models and enabling predictive analytics monitoring.

How does data observability improve data contract enforcement?

Data observability adds critical context that static contracts lack, such as data lineage tracking, real-time usage patterns, and anomaly detection. With observability tools, teams can proactively monitor contract compliance, detect schema drift early, and ensure SLA compliance before issues impact downstream systems. It transforms contracts from documentation into enforceable, living agreements.

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