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
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Frequently asked questions

How does SQL Table Tracer support different SQL dialects for data lineage tracking?
SQL Table Tracer uses Antlr4 and a unified grammar with semantic predicates to support multiple SQL dialects like Snowflake, Redshift, and PostgreSQL. This ensures accurate data lineage tracking across diverse systems without needing separate parsers for each dialect.
Why is data observability important for large organizations?
Data observability helps organizations ensure data quality, monitor pipelines in real time, and build trust in their data. At Big Data LDN, we’ll share how companies like Penguin Random House use observability tools to improve data governance and drive better decisions.
What makes Sifflet different from other data observability tools?
Sifflet stands out as a metadata control plane that connects technical reliability with business context. Unlike point solutions, it offers AI-native automation, full data lineage tracking, and cross-functional accessibility, making it ideal for organizations that need to scale trust in their data across teams.
How does Sifflet support root cause analysis when a deviation is detected?
Sifflet combines distribution deviation monitoring with field-level data lineage tracking. This means when an anomaly is detected, you can quickly trace it back to the source and resolve it efficiently. It’s a huge time-saver for teams managing complex data pipeline monitoring.
Why is data observability so important for AI and analytics initiatives?
Great question! Data observability ensures that the data fueling AI and analytics is reliable, accurate, and fresh. At Sifflet, we see data observability as both a technical and business challenge, which is why our platform focuses on data quality monitoring, anomaly detection, and real-time metrics to help enterprises make confident, data-driven decisions.
Why did Adaptavist choose Sifflet over other observability tools?
Callum and his team were impressed by how quickly Sifflet’s cross-repo data lineage tracking gave them visibility into their pipelines. Within days, they had a working proof of concept and were debugging in minutes instead of days. The unified view across their stack made Sifflet the right fit for scaling data observability across teams.
How can data observability help improve the happiness of my data team?
Great question! A strong data observability platform helps reduce uncertainty in your data pipelines by providing transparency, real-time metrics, and proactive anomaly detection. When your team can trust the data and quickly identify issues, they feel more confident, empowered, and less stressed, which directly boosts team morale and satisfaction.
How does data quality monitoring help improve data reliability?
Data quality monitoring is essential for maintaining trust in your data. A strong observability platform should offer features like anomaly detection, data profiling, and data validation rules. These tools help identify issues early, so you can fix them before they impact downstream analytics. It’s all about making sure your data is accurate, timely, and reliable.