Solution by Use Case

Cloud migration monitoring

Mitigate disruption and risks

Customers choose Sifflet for migrations because it unifies lineage, monitoring, and triage in one place, giving teams clear, business-relevant insights without tool-switching. Its AI speeds up root cause analysis, learns your environment, and cuts manual effort, typically going live in under an hour and scaling fully in six weeks.

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Pre-Migration: Baseline and Prepare

Create a complete inventory and establish trust baselines before any data is moved.

‍What Sifflet enables

• End-to-end lineage mapping across your on-prem estate, so you know exactly which tables, dashboards, and KPIs depend on each other before changing pipelines.

• Automated data profiling and health scoring to establish quality baselines (volumes, distributions, freshness, schema shape) for every critical asset.

• Domain-level ownership so each business area knows its scope and responsibilities ahead of the migration.

• Monitors as Code to version and package all checks that will run pre- and post-migration.

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‍Outcome: A clear, auditable understanding of what “good” looks like before the first batch of data is moved.

During Migration: Parallel Validation and Controlled Cutover

Continuously validate data between your on-prem and Snowflake environments.

‍What Sifflet enables

• Automated cross-environment comparison checks using custom SQL monitors, dynamic tests, and Sifflet’s failing-rows view.

• Adaptive anomaly detection with seasonal awareness to catch regressions introduced by new pipelines or refactored logic.

• Incident-centric workflow to consolidate related alerts, generate AI-driven root cause analysis, and route to the right domain team.

• Field-level lineage to understand the blast radius of every upstream change as migration waves progress.

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‍Outcome: Fast detection of mismatches, broken joins, missing data, or schema drift without manual spot-checking.

Post-Migration: Stabilise and Scale

Ensure production-grade reliability in Snowflake after cutover.

‍What Sifflet enables

• Auto-coverage and Monitor Recommendations (Sentinel) to close blind spots and automatically instrument new Snowflake tables.

• BI-embedded notifications (Power BI, Tableau, Looker) to alert business teams when downstream metrics change.

• Data Product views and SLAs to formalise trust in the new ecosystem and expose quality metrics to stakeholders.

• Cost-efficient observability with workload tagging and percent compute overhead to keep Snowflake spend predictable.

‍Outcome: A stable, trusted Snowflake environment with observability built in, not bolted on.

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

How does Shippeo ensure data reliability across its supply chain platform?

Shippeo uses Sifflet’s data observability platform to monitor every stage of their data pipelines. By implementing raw data monitoring, intermediate layer checks, and front-facing metric validation, they catch issues early and maintain trust in their real-time supply chain visibility tools.

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 field-level lineage important in data observability?

Field-level lineage gives you a detailed view into how individual data fields move and transform through your pipelines. This level of granularity is super helpful for root cause analysis and understanding the impact of changes. A platform with strong data lineage tracking helps teams troubleshoot faster and maintain high data quality.

What makes Etam’s data strategy resilient in a fast-changing retail landscape?

Etam’s data strategy is built on clear business alignment, strong data quality monitoring, and a focus on delivering ROI across short, mid, and long-term horizons. With the help of an observability platform, they can adapt quickly, maintain data reliability, and support strategic decision-making even in uncertain conditions.

What role does data observability play in modern data governance?

AI enhances data observability with advanced anomaly detection, predictive analytics, and automated root cause analysis. This helps teams identify and resolve issues faster while reducing manual effort. Have a look at how Sifflet is leveraging AI for better data observability here

Why is data lineage important for GDPR compliance?

Data lineage is essential for GDPR because it helps you trace personal data from source to destination. This means you can see where PII is stored, how it flows through your data pipelines, and which reports or applications use it. With this visibility, you can manage deletion requests, audit data usage, and ensure data governance policies are enforced consistently.

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.

What are some best practices for ensuring data quality during transformation?

To ensure high data quality during transformation, start with strong data profiling and cleaning steps, then use mapping and validation rules to align with business logic. Incorporating data lineage tracking and anomaly detection also helps maintain integrity. Observability tools like Sifflet make it easier to enforce these practices and continuously monitor for data drift or schema changes that could affect your pipeline.

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