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

How does the improved test connection process for Snowflake observability help teams?

The revamped 'Test Connection' process for Snowflake observability now provides detailed feedback on missing permissions or policy issues. This makes setup and troubleshooting much easier, especially during onboarding. It helps ensure smooth data pipeline monitoring and reduces the risk of refresh failures down the line.

What is data lineage and why is it important for data observability?

Data lineage is the process of tracing data as it moves from source to destination, including all transformations along the way. It's a critical component of data observability because it helps teams understand dependencies, troubleshoot issues faster, and maintain data reliability across the entire pipeline.

What is data observability and why is it important?

Data observability is the ability to monitor, understand, and troubleshoot data systems using real-time metrics and contextual insights. It's important because it helps teams detect and resolve issues quickly, ensuring data reliability and reducing the risk of bad data impacting business decisions.

What kind of insights can I gain by integrating Airbyte with Sifflet?

By integrating Airbyte with Sifflet, you unlock real-time insights into your data pipelines, including data freshness checks, anomaly detection, and complete data lineage tracking. This helps improve SLA compliance, reduces troubleshooting time, and boosts your confidence in data quality and pipeline health.

How does Sifflet enhance metadata catalogs with data observability?

Sifflet enriches your metadata catalog by integrating real-time data observability signals like freshness metrics, anomaly detection, and lineage updates. This means your catalog stays current as your data changes, helping you catch issues faster and maintain high data reliability. It's a great example of combining observability tools with metadata management for smarter data operations.

Who should be responsible for managing data quality in an organization?

Data quality management works best when it's a shared responsibility. Data stewards often lead the charge by bridging business needs with technical implementation. Governance teams define standards and policies, engineering teams build the monitoring infrastructure, and business users provide critical domain expertise. This cross-functional collaboration ensures that quality issues are caught early and resolved in ways that truly support business outcomes.

Why is data quality monitoring crucial for AI-readiness, according to Dailymotion’s journey?

Dailymotion emphasized that high-quality, well-documented, and observable data is essential for AI readiness. Data quality monitoring ensures that AI systems are trained on accurate and reliable inputs, which is critical for producing trustworthy outcomes.

Why did jobvalley choose Sifflet over other data catalog vendors?

After evaluating several data catalog vendors, jobvalley selected Sifflet because of its comprehensive features that addressed both data discovery and data quality monitoring. The platform’s ability to streamline onboarding and support real-time metrics made it the ideal choice for their growing data team.

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Proactive access, quality and control