Product

Data Catalog

Search, Shop and Adopt %%Your Data%%

Everyone’s more productive when they can discover, browse, preview and adopt the data they need with confidence, all from one spot.

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Intelligent by Design

At last, a data catalog that’s smart. Powered by algorithms that make it easy to find what you’re looking for in seconds and LLM-assisted documentation and classification recommendations that can even detect PII.

Nothing But the Truth

From a business glossary to centralized metadata, give everyone a single source of truth. And you’ll never question data accuracy, freshness or reliability thanks to built-in monitoring.

Easy to Connect and Use

The moment you open your data catalog, it’s ready for whatever you need. Whether you’re on the product team and want to understand how churn rate is computed or a business analyst in search of the right data source, intuitive UI means everyone can collaborate.

BROWSE

Single Source of Truth

A one-stop shop for data knowledge at your company.

E2E with OOTB cataloguing and declarative
Maintain data documentation and classification thanks to GenAI assisted asset descriptions that can detect PII
Create a business glossary so everyone’s on the same page
Preview your data in one click

SHOP

Smart Data Assets Search

Find and adopt the data you need for your work, in record time.

Simplify discovery with smart data sorting algorithms
Segment data access for business domains
Use the Sifflet Insights browser extension while you work

TRUST

Built-In Monitoring

When monitoring is built in, you’ll never question data freshness, accuracy, or reliability.

Enable data mesh and data self-serve thanks to built-in monitoring and data asset health status
Enhance and assess monitoring coverage with filtering options

Reinforced %%Reliability%%

Sifflet’s monitoring features reinforce data reliability for all users, so business can deliver.

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Data Users

Find the data you need when you need it, understand what data powers your dashboards, and make strategic recommendations and plans with confidence.

Data Engineers

Sifflet’s catalog is embedded in a data observability platform, not the other way around. That means you are better equipped to ensure reliability and quality than with a standalone catalog.

Data Leaders

Improve your team’s productivity by giving them back up to 40% of the time they spend looking for the right data and vetting quality and empower business owners with clean documentation.

Looking for more?

Data Catalog

Your data catalog is not enough

Customer Stories

How Carrefour Transformed Data Quality

sifflet a guide to assessing your need for a data observability

Insights

A Guide to Assessing your Need for Data Observability

Drive Data Adoption Now

Sifflet makes sure your teams never question the accuracy, freshness, or quality of assets in your catalog.

Speak With Our Experts

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

Still have a question in mind ?

Frequently asked questions

What are some common data quality issues that can be prevented with the right tools?

Common issues like schema changes, missing values, and data drift can all be caught early with effective data quality monitoring. Tools that offer features like threshold-based alerts, data freshness checks, and pipeline health dashboards make it easier to prevent these problems before they affect downstream systems.

Why are data consumers becoming more involved in observability decisions?

We’re seeing a big shift where data consumers—like analysts and business users—are finally getting a seat at the table. That’s because data observability impacts everyone, not just engineers. When trust in data is operationalized, it boosts confidence across the business and turns data teams into value creators.

How can data teams prioritize what to monitor in complex environments?

Not all data is created equal, so it's important to focus data quality monitoring efforts on the assets that drive business outcomes. That means identifying key dashboards, critical metrics, and high-impact models, then using tools like pipeline health dashboards and SLA monitoring to keep them reliable and fresh.

Why is a data catalog essential for modern data teams?

A data catalog is critical because it helps teams find, understand, and trust their data. It centralizes metadata, making data assets searchable and understandable, which reduces duplication, speeds up analytics, and supports data governance. When paired with data observability tools, it becomes a powerful foundation for proactive data management.

What makes Sifflet's architecture unique for secure data pipeline monitoring?

Sifflet uses a cell-based architecture that isolates each customer’s instance and database. This ensures that even under heavy usage or a potential breach, your data pipeline monitoring remains secure, reliable, and unaffected by other customers’ activities.

How can organizations create a culture that supports data observability?

Fostering a data-driven culture starts with education and collaboration. Salma recommends training programs that boost data literacy and initiatives that involve all data stakeholders. This shared responsibility approach ensures better data governance and more effective data quality monitoring.

How do the four pillars of data observability help improve data quality?

The four pillars—metrics, metadata, data lineage, and logs—work together to give teams full visibility into their data systems. Metrics help with data profiling and freshness checks, metadata enhances data governance, lineage enables root cause analysis, and logs provide insights into data interactions. Together, they support proactive data quality monitoring.

How does Sifflet use AI to improve data classification?

Sifflet leverages machine learning to provide AI Suggestions for classification tags, helping teams automatically identify and label key data characteristics like PII or low cardinality. This not only streamlines data management but also enhances data quality monitoring by reducing manual effort and human error.

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