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 Sifflet stand out among other data observability tools?
Sifflet takes a unique approach by addressing data reliability as both an engineering and business challenge. Our observability platform offers end-to-end coverage, business context, and a collaboration layer that aligns technical teams with strategic outcomes, making it easier to maintain analytics and AI-ready data.
Can I monitor my BigQuery data with Sifflet?
Absolutely! Sifflet’s observability tools are fully compatible with Google BigQuery, so you can perform data quality monitoring, data lineage tracking, and anomaly detection right where your data lives.
What kinds of metrics can retailers track with advanced observability tools?
Retailers can track a wide range of metrics such as inventory health, stock obsolescence risks, carrying costs, and dynamic safety stock levels. These observability dashboards offer time-series analysis and predictive insights that support better decision-making and improve overall data reliability.
How does Sifflet use AI to improve data classification?
Sifflet leverages machine learning to provide AI Suggestions for classification tags, helping teams automatically identify and label key data characteristics like PII or low cardinality. This not only streamlines data management but also enhances data quality monitoring by reducing manual effort and human error.
What makes Sifflet’s Data Catalog different from built-in catalogs like Snowsight or Unity Catalog?
Unlike tool-specific catalogs, Sifflet serves as a 'Catalog of Catalogs.' It brings together metadata from across your entire data ecosystem, providing a single source of truth for data lineage tracking, asset discovery, and SLA compliance.
How can organizations improve data governance with modern observability tools?
Modern observability tools offer powerful features like data lineage tracking, audit logging, and schema registry integration. These capabilities help organizations improve data governance by providing transparency, enforcing data contracts, and ensuring compliance with evolving regulations like GDPR.
What role does Sifflet’s Data Catalog play in data governance?
Sifflet’s Data Catalog supports data governance by surfacing labels and tags, enabling classification of data assets, and linking business glossary terms for standardized definitions. This structured approach helps maintain compliance, manage costs, and ensure sensitive data is handled responsibly.
How does Sifflet support reverse ETL and operational analytics?
Sifflet enhances reverse ETL workflows by providing data observability dashboards and real-time monitoring. Our platform ensures your data stays fresh, accurate, and actionable by enabling root cause analysis, data lineage tracking, and proactive anomaly detection across your entire pipeline.
Still have questions?