Solution by Use Case

Cost Observability

Cost-efficient data pipelines

Pinpoint cost inefficiencies and anomalies thanks to full-stack data observability.

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Data asset optimization

Leverage lineage and Data Catalog to pinpoint underutilized assets
Get alerted on unexpected behaviors in data consumption patterns

Proactive data pipeline management

Proactively prevent pipelines from running in case a data quality anomaly is detected

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 data observability differ from traditional data quality monitoring?

Great question! While data quality monitoring focuses on detecting when data doesn't meet expected thresholds, data observability goes further. It continuously collects signals like metrics, metadata, and lineage to provide context and root cause analysis when issues arise. Essentially, observability helps you not only detect anomalies but also understand and fix them faster, making it a more proactive and scalable approach.

What does it mean to treat data as a product?

Treating data as a product means managing data with the same care and strategy as a traditional product. It involves packaging, maintaining, and delivering high-quality data that serves a specific purpose or audience. This approach improves data reliability and makes it easier to monetize or use for strategic decision-making.

When should I consider using a point solution like Anomalo or Bigeye instead of a full observability platform?

If your team has a narrow focus on anomaly detection or prefers a SQL-first, hands-on approach to monitoring, tools like Anomalo or Bigeye can be great fits. However, for broader needs like data governance, business impact analysis, and cross-functional collaboration, a platform like Sifflet offers more comprehensive data observability.

How does Flow Stopper support root cause analysis and incident prevention?

Flow Stopper enables early anomaly detection and integrates with your orchestrator to halt execution when issues are found. This makes it easier to perform root cause analysis before problems escalate and helps prevent incidents that could affect business-critical dashboards or KPIs.

What makes Sifflet a more inclusive data observability platform compared to Monte Carlo?

Sifflet is designed for both technical and non-technical users, offering no-code monitors, natural-language setup, and cross-persona alerts. This means analysts, data scientists, and executives can all engage with data quality monitoring without needing engineering support, making it a truly inclusive observability platform.

Is there a networking opportunity with the Sifflet team at Big Data Paris?

Yes, we’re hosting an exclusive after-party at our booth on October 15! Come join us for great conversations, a champagne toast, and a chance to connect with data leaders who care about data governance, pipeline health, and building resilient systems.

What benefits does end-to-end data lineage offer my team?

End-to-end data lineage helps your team perform accurate impact assessments and faster root cause analysis. By connecting declared and built-in assets, you get full visibility into upstream and downstream dependencies, which is key for data reliability and operational intelligence.

How can I prevent schema changes from breaking my data pipelines?

You can prevent schema-related breakages by using data observability tools that offer real-time schema drift detection and alerting. These tools help you catch changes early, validate against data contracts, and maintain SLA compliance across your data pipelines.

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