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Data Users

Thanks to AI, there’s no need to wait for the data engineering team to adapt, create or fix a monitor. Your monitors can also adapt to changes in seasonal trends. 

Data Engineers

Sifflet’s AI helps reduce manual work on tedious, repetitive tasks and gives your data users self-serve tools instead of requiring engineering time.

Data Leaders

AI features that make your data engineers more efficient and your data users better able to take ownership of their data.

Data Users

Enhance customer trust with tools that secure data and help your business align with regulatory and compliance requirements.

Data Engineers

Keep specific data pipelines and storage systems confidential and protect critical data assets from manipulation.

Data Leaders

Avoid legal issues, financial penalties and reputational damage associated with data mishandling, or unauthorized data access and breaches.

Data Users

Stop working with corrupt data. Sifflet embeds alerts in your dashboards, so you know exactly when there’s an incident or issue. And you can set up data monitors on your own.

Data Engineers

No more scaling monitors manually. Sifflet’s ML will optimize coverage for you, so you can be proactive instead of reactive in reducing downtimes.

Data Leaders

Give your teams the tools they need to reduce monitoring tasks by up to 50% thanks to Sifflet’s monitoring features.

Data Engineers

With Sifflet’s lineage, get up to 50% of the time you spend on mundane reliability tasks back and gain insight into your data across the entire lifecycle.

Data Leaders

Reduce data downtime and help the whole company benefit from better data quality by ensuring your teams can get to the bottom of root causes, faster.

Data Users

Understand where your data comes from to make informed decisions and break down silos between teams.

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.

Data Leaders

Drive innovation and enable AI. With Sifflet, you can transform your data strategy, governance, and team productivity while ensuring efficient and scalable data infrastructure.

Data Engineers

Boost your productivity. Sifflet gives you end-to-end visibility into your architecture, assets, and pipelines. Advanced monitoring ensures you get the right alerts and lineage helps you get to resolution faster.

Data Users

No more data discrepancies. Sifflet ensures the highest levels of data quality. Your teams can make the best possible decisions for your company, unlocking new levels of performance that help you compete in the age of AI.

Get in touch CTA Section

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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

Frequently asked questions

How does Sifflet help with SLA compliance and incident response?
Sifflet supports SLA compliance by offering intelligent alerting, dynamic thresholding, and real-time dashboards that track incident metrics and resolution times. Its data reliability dashboard gives teams visibility into SLA adherence and helps prioritize issues based on business impact, streamlining incident management workflows and reducing mean time to resolution.
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 do declared assets improve data quality monitoring?
Declared assets appear in your Data Catalog just like built-in assets, with full metadata and business context. This improves data quality monitoring by making it easier to track data lineage, perform data freshness checks, and ensure SLA compliance across your entire pipeline.
How does Sifflet support data quality monitoring at scale?
Sifflet uses AI-powered dynamic monitors and data validation rules to automate data quality monitoring across your pipelines. It also integrates with tools like Snowflake and dbt to ensure data freshness checks and schema validations are embedded into your workflows without manual overhead.
What best practices should I follow when planning for data quality monitoring?
Start by defining data validation rules and ownership early in your architecture. Use observability tools that support proactive monitoring, anomaly detection, and root cause analysis to catch issues before they affect downstream systems or business decisions.
How does Sifflet’s revamped dbt integration improve data observability?
Great question! With our latest dbt integration update, we’ve unified dbt models and the datasets they generate into a single asset. This means you get richer context and better visibility across your data pipelines, making it easier to track data lineage, monitor data quality, and ensure SLA compliance all from one place.
Who are some of the companies using Sifflet’s observability tools?
We're proud to work with amazing organizations like St-Gobain, Penguin Random House, and Euronext. These enterprises rely on Sifflet for cloud data observability, data lineage tracking, and proactive monitoring to ensure their data is always AI-ready and analytics-friendly.
How can poor data distribution impact machine learning models?
When data distribution shifts unexpectedly, it can throw off the assumptions your ML models are trained on. For example, if a new payment processor causes 70% of transactions to fall under $5, a fraud detection model might start flagging legitimate behavior as suspicious. That's why real-time metrics and anomaly detection are so crucial for ML model monitoring within a good data observability framework.
Still have questions?