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 Sifflet support enterprises with data pipeline monitoring?

Sifflet provides a comprehensive observability platform that monitors the health of data pipelines through features like pipeline error alerting, data freshness checks, and ingestion latency tracking. This helps teams identify issues early and maintain SLA compliance across their data workflows.

How does Sifflet support data pipeline monitoring at Carrefour?

Sifflet enables comprehensive data pipeline monitoring through features like monitoring-as-code and seamless integration with data lineage tracking and governance tools. This gives Carrefour full visibility into their pipeline health and helps ensure SLA compliance.

Can data lineage help with regulatory compliance like GDPR?

Absolutely. Governance lineage, a key type of data lineage, tracks ownership, access controls, and data classifications. This makes it easier to demonstrate compliance with regulations like GDPR and SOX by showing how sensitive data is handled across your stack. It's a critical component of any data governance strategy and helps reduce audit preparation time.

How does Sifflet help with analytics tools like Looker?

Sifflet extends its end-to-end data observability to Looker, helping you ensure the data powering your dashboards is accurate and reliable. This means fewer surprises and more confidence in your business insights.

How can observability platforms help with compliance and audit logging?

Observability platforms like Sifflet support compliance monitoring by tracking who accessed what data, when, and how. We help teams meet GDPR, NERC CIP, and other regulatory requirements through audit logging, data governance tools, and lineage visibility. It’s all about making sure your data is not just stored safely but also traceable and verifiable.

What is the Universal Connector and how does it support data pipeline monitoring?

The Universal Connector lets you integrate Sifflet with any tool in your stack using YAML and API endpoints. It enables full-stack data pipeline monitoring and data lineage tracking, even for tools Sifflet doesn’t natively support, offering a more complete view of your observability workflows.

How can I track the success of my data team?

Define clear success KPIs that support ROI, such as improvements in SLA compliance, reduction in ingestion latency, or increased data reliability. Using data observability dashboards and pipeline health metrics can help you monitor progress and communicate value to stakeholders. It's also important to set expectations early and maintain strong internal communication.

How does data observability help improve data reliability?

Data observability gives you end-to-end visibility into your data pipelines, helping you catch issues like schema changes, data drift, or ingestion failures before they impact downstream systems. By continuously monitoring real-time metrics and enabling root cause analysis, observability platforms like Sifflet ensure your data stays accurate, complete, and up-to-date, which directly supports stronger data reliability.

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