MONITOR

Lean. Mean. Monitoring Machine. 

Finally, dynamic monitoring that can keep up with your stack. AI features optimize your coverage and minimize noise, detecting issues before they arise. 

Sifflet dashboard features overview

Customize to Your Heart’s Content

Sifflet offers both a robust library of out of the box monitors and customization capability. Your teams decide what needs monitoring and how to set it up. 

Bye-Bye, Alert Fatigue

Data engineers don’t need more alerts, they need smarter alerts. Our AI learns adaptively as it goes to optimize coverage and minimize noise.  

Hello, Data Reliability 

Data reliability is reinforced with less manual work for technical teams, faster response times, and overall stronger performance. 

IMPLEMENT

Ready-to-Go Monitors 

Quick set up and implementation means quicker results. 

  • See value instantly with pre-defined templates to check data at field and table levels
  • Help your business users and technical teams meet their quality and reliability objectives thanks to ready-to-go monitors
Sifflet dashboard overview
SUPERVISE

Lifecycle Monitoring

End-to-end coverage that never sleeps. 

  • Detect anomalies continuously thanks to ML models 
  • Give your business users ownership over monitors through LLM monitoring setup 
  • Maintain control and accuracy with optional manual setup and user feedback
Sifflet dashboard features overview
MAINTAIN

Scalability & Optimization

Monitoring that’s easy to maintain and coverage that’s just right.

  • Optimize monitoring coverage and minimize noise levels with AI-powered suggestions and supervision
  • Implement programmatic monitoring set up and maintenance with Data Quality as Code (DQaC)
Sifflet dashboard overview
TEAMS

Reinforced Reliability

Built for Everyone

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

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 Reliability, Reinforced

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

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 data observability help ensure SLA compliance for data products?
Data observability plays a big role in SLA compliance by continuously monitoring data freshness, quality, and availability. With tools like Sifflet, teams can set alerts and track metrics that align with their SLAs, ensuring data products meet business expectations consistently.
Can Sifflet help with data quality monitoring directly from the Data Catalog?
Absolutely! Sifflet integrates data quality monitoring into its Data Catalog, allowing users to define and view data quality checks right alongside asset metadata. This gives teams real-time insights into data reliability and helps build trust in the assets they’re using for decision-making.
How can business teams benefit from using Sifflet Insights?
Business teams can access data quality insights directly within their BI dashboards, reducing their reliance on data engineers. This democratizes data observability and empowers teams to make confident, data-driven decisions with full transparency into data lineage and reliability.
What does Sifflet plan to do with the new $18M in funding?
We're excited to use this funding to accelerate product innovation, expand our North American presence, and grow our team. Our focus will be on enhancing AI-powered capabilities, improving data pipeline monitoring, and helping customers maintain data reliability at scale.
What role do Common Table Expressions (CTEs) play in query optimization?
CTEs help simplify complex queries by breaking them into manageable parts. This boosts readability and performance, making it easier to identify issues during root cause analysis and enhancing your data quality monitoring efforts.
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 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 Sifflet help with SLA compliance for business metrics?
By combining real-time metrics monitoring with proactive alerts and incident management workflows, Sifflet helps teams stay on top of SLA compliance. Users can track metrics freshness, detect anomalies, and take action before SLA breaches occur.
Still have questions?