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

Reinforced %%Reliability%%

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.

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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.

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Data Leaders

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

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Data Reliability, %%Reinforced%%

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

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

Why is schema monitoring such a critical part of data observability?
Schema monitoring helps catch unexpected changes in your data structure before they break downstream systems like dashboards or ML models. It's a core capability in any modern observability platform because it ensures data reliability and prevents silent failures in your pipelines.
How does Sifflet make it easier to manage data volume at scale?
Sifflet simplifies data volume monitoring with plug-and-play integrations, AI-powered baselining, and unified observability dashboards. It automatically detects anomalies, connects them to business impact, and provides real-time alerts. Whether you're using Snowflake, BigQuery, or Kafka, Sifflet helps you stay ahead of data reliability issues with proactive monitoring and alerting.
What is metrics observability and why does it matter for business users?
Metrics observability helps business users trust and understand the KPIs they rely on by making it easy to trace how metrics are defined, calculated, and connected to other data assets. With Sifflet’s observability platform, teams can ensure their business metrics are accurate, reliable, and aligned across departments.
Can Sifflet integrate with our existing data tools and platforms?
Absolutely! Sifflet is designed to integrate seamlessly with your current stack. We support a wide range of tools including Airflow, Snowflake, AWS Glue, and more. Our goal is to provide complete pipeline orchestration visibility and data freshness checks, all from one intuitive interface.
Does Sifflet support AI-driven use cases?

Yes, Sifflet leverages AI to enhance data observability with features like anomaly detection and predictive insights. This ensures your data systems remain resilient and can support advanced analytics and AI-driven initiatives. Have a look at how Sifflet is leveraging AI for better data observability here

What does it mean to treat data as a product?
Treating data as a product means prioritizing its reliability, usability, and trustworthiness—just like you would with any customer-facing product. This mindset shift is driving the need for observability platforms that support data governance, real-time metrics, and proactive monitoring across the entire data lifecycle.
What role does data lineage tracking play in volume monitoring?
Data lineage tracking is essential for root cause analysis when volume anomalies occur. It helps you trace where data came from and how it's been transformed, so if a volume drop happens, you can quickly identify whether it was caused by a failed API, upstream filter, or schema change. This context is key for effective data pipeline monitoring.
Why is the traditional approach to data observability no longer enough?
Great question! The old playbook for data observability focused heavily on technical infrastructure and treated data like servers — if the pipeline ran and the schema looked fine, the data was assumed to be trustworthy. But today, data is a strategic asset that powers business decisions, AI models, and customer experiences. At Sifflet, we believe modern observability platforms must go beyond uptime and freshness checks to provide context-aware insights that reflect real business impact.