Solution by Use Case

Data governance

Proactive access, quality and control

Empower data teams to detect and address issues proactively by providing them with tools to ensure data availability, usability, integrity, and security.

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De-risked data discovery

Ensure proactive data quality thanks to a large library of OOTB monitors and a built-in notification system
Gain visibility over assets’ documentation and health status on the Data Catalog for safe data discovery
Establish the official source of truth for key business concepts using the Business Glossary
Leverage custom tagging to classify assets

Structured data observability platform

Tailor data visibility for teams by grouping assets in domains that align with the company’s structure
Define data ownership to improve accountability and smooth collaboration across teams

Secured data management

Safeguard PII data securely through ML-based PII detection

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

Who are some of the companies using Sifflet’s observability tools?

We're proud to work with amazing organizations like St-Gobain, Penguin Random House, and Euronext. These enterprises rely on Sifflet for cloud data observability, data lineage tracking, and proactive monitoring to ensure their data is always AI-ready and analytics-friendly.

What should a solid data quality monitoring framework include?

A strong data quality monitoring framework should be scalable, rule-based and powered by AI for anomaly detection. It should support multiple data sources and provide actionable insights, not just alerts. Tools that enable data drift detection, schema validation and real-time alerts can make a huge difference in maintaining data integrity across your pipelines.

How does Sifflet support data lineage tracking and context enrichment?

Sifflet enhances your data catalog with lineage tracking and context by incorporating dbt model descriptions, input-output dataset views, and AI-powered recommendations. This enrichment helps users quickly understand where data comes from and how it's used, making it easier to trust and leverage data confidently.

Why is integration with my existing tools important for observability?

A good observability platform should fit right into your current stack. That means supporting tools like dbt, Airflow, and your cloud infrastructure. Seamless integration ensures better pipeline orchestration visibility and makes it easier to act on data issues without disrupting your workflows.

Can Sifflet help with data quality monitoring directly from the Data Catalog?

Absolutely! Sifflet integrates data quality monitoring into its Data Catalog, allowing users to define and view data quality checks right alongside asset metadata. This gives teams real-time insights into data reliability and helps build trust in the assets they’re using for decision-making.

What is the Model Context Protocol (MCP), and why is it important for data observability?

The Model Context Protocol (MCP) is a new interface standard developed by Anthropic that allows large language models (LLMs) to interact with tools, retain memory, and access external context. At Sifflet, we're excited about MCP because it enables more intelligent agents that can help with data observability by diagnosing issues, triggering remediation tools, and maintaining context across long-running investigations.

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.

What new investments is Sifflet making after the latest funding round?

We're excited to be investing in four key areas: enhancing our product roadmap, expanding our AI-powered capabilities, growing our North American presence, and accelerating hiring across teams. These efforts will help us continue leading in cloud data observability and better serve our growing customer base.

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Proactive access, quality and control