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

What role does real-time data play in modern analytics pipelines?
Real-time data is becoming a game-changer for analytics, especially in use cases like fraud detection and personalized recommendations. Streaming data monitoring and real-time metrics collection are essential to harness this data effectively, ensuring that insights are both timely and actionable.
How did Dailymotion use data observability to support their shift to a product-oriented data platform?
Dailymotion embedded data observability into their data ecosystem to ensure trust, reliability, and discoverability across teams. This shift allowed them to move from ad hoc data requests to delivering scalable, analytics-driven data products that empower both engineers and business users.
How does data observability complement a data catalog?
While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.
Why is data observability so important for AI-powered organizations in 2025?
Great question! As AI continues to evolve, the quality and reliability of the data feeding those models becomes even more critical. Data observability ensures that your AI systems are powered by clean, accurate, and up-to-date data. With platforms like Sifflet, organizations can detect issues like data drift, monitor real-time metrics, and maintain data governance, all of which help AI models stay accurate and trustworthy.
What role does data quality monitoring play in a successful data management strategy?
Data quality monitoring is essential for maintaining the integrity of your data assets. It helps catch issues like missing values, inconsistencies, and outdated information before they impact business decisions. Combined with data observability, it ensures that your data catalog reflects trustworthy, high-quality data across the pipeline.
Why is data quality so critical for businesses today?
Great question! Data quality is essential because it directly influences decision-making, customer satisfaction, and operational efficiency. Poor data quality can lead to faulty insights, wasted resources, and even reputational damage. That's why many teams are turning to data observability platforms to ensure their data is accurate, complete, and trustworthy across the entire pipeline.
How does the shift to poly cloud impact observability platforms?
The move toward poly cloud environments increases the complexity of monitoring, but observability platforms are evolving to unify insights across multiple cloud providers. This helps teams maintain SLA compliance, monitor ingestion latency, and ensure data reliability regardless of where workloads are running.
What are the five technical pillars of data observability?
The five technical pillars are freshness, volume, schema, distribution, and lineage. These cover everything from whether your data is arriving on time to whether it still follows expected patterns. A strong observability tool like Sifflet monitors all five, providing real-time metrics and context so you can quickly detect and resolve issues before they cause downstream chaos.