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.

Sifflet dashboard features overview

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
Sifflet dashboard overview
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
Sifflet dashboard overview
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
Sifflet dashboard overview

Reinforced %%Reliability%%

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

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.

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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.

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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.

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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 ?
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Frequently asked questions

How do I choose the right organizational structure for my data team?
It depends on your company's size, data maturity, and use cases. Some teams report to engineering or product, while others operate as independent entities reporting to the CEO or CFO. The key is to avoid silos and unclear ownership. A centralized or hybrid structure often works well to promote collaboration and maintain transparency in data pipelines.
What role did data observability play in Carrefour’s customer engagement strategy?
Data observability was crucial in maintaining high data quality for loyalty programs and marketing campaigns. With real-time metrics and anomaly detection in place, Carrefour was able to improve customer satisfaction and retention through more accurate and timely insights.
Which ingestion tools work best with cloud data observability platforms?
Popular ingestion tools like Fivetran, Stitch, and Apache Kafka integrate well with cloud data observability platforms. They offer strong support for telemetry instrumentation, real-time ingestion, and schema registry integration. Pairing them with observability tools ensures your data stays reliable and actionable across your entire stack.
What role does Sifflet’s data catalog play in observability?
Sifflet’s data catalog acts as the central hub for your data ecosystem, enriched with metadata and classification tags. This foundation supports cloud data observability by giving teams full visibility into their assets, enabling better data lineage tracking, telemetry instrumentation, and overall observability platform performance.
Can Sifflet help with root cause analysis in complex data systems?
Absolutely! In early 2025, we're rolling out advanced root cause analysis tools designed to help you detect subtle anomalies and trace them back to their source. Whether the issue lies in your code, data, or pipelines, our observability platform will help you get to the bottom of it faster.
How does Sifflet make setting up data quality monitoring easier?
Great question! With the launch of Data-Quality-as-Code v2, Sifflet has made it much easier to create and manage monitors at scale. Whether you prefer working programmatically or through the UI, our platform now offers smoother workflows and standardized threshold settings for more intuitive data quality monitoring.
What practical steps can companies take to build a data-driven culture?
To build a data-driven culture, start by investing in data literacy, aligning goals across teams, and adopting observability tools that support proactive monitoring. Platforms with features like metrics collection, telemetry instrumentation, and real-time alerts can help ensure data reliability and build trust in your analytics.
What should I consider when choosing a data observability tool?
When selecting a data observability tool, consider your data stack, team size, and specific needs like anomaly detection, metrics collection, or schema registry integration. Whether you're looking for open source observability options or a full-featured commercial platform, make sure it supports your ecosystem and scales with your data operations.