Product

Data Quality Monitoring

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.

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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

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

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)

Reinforced %%Reliability%%

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

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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.

Looking for more?

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Insights

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Data Monitoring

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Data Monitoring

Data monitoring vs observability solutions

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 ?

Frequently asked questions

How does integrating dbt with Sifflet improve data observability?

Great question! When you integrate dbt with Sifflet, you unlock a whole new level of data observability. Sifflet enhances visibility into your dbt models by pulling in metadata, surfacing test results, and mapping them into a unified lineage view. This makes it easier to monitor data pipelines, catch issues early, and ensure data reliability across your organization.

What kinds of alerts can trigger incidents in ServiceNow through Sifflet?

You can trigger incidents from any Sifflet alert, including data freshness checks, schema changes, and pipeline failures. This makes it easier to maintain SLA compliance and improve overall data reliability across your observability platform.

Is this feature part of Sifflet’s larger observability platform?

Yes, dbt Impact Analysis is a key addition to Sifflet’s observability platform. It integrates seamlessly into your GitHub or GitLab workflows and complements other features like data lineage tracking and data quality monitoring to provide holistic data observability.

How does Sifflet help with data drift detection in machine learning models?

Great question! Sifflet's distribution deviation monitoring uses advanced statistical models to detect shifts in data at the field level. This helps machine learning engineers stay ahead of data drift, maintain model accuracy, and ensure reliable predictive analytics monitoring over time.

How does Sifflet support diversity and innovation in the data observability space?

Diversity and innovation are core values at Sifflet. We believe that a diverse team brings a wider range of perspectives, which leads to more creative solutions in areas like cloud data observability and predictive analytics monitoring. Our culture encourages experimentation and continuous learning, making it a great place to grow.

Is this integration useful for teams focused on data governance and compliance?

Yes, it really is! With enhanced lineage and metadata tracking from source to destination, the Fivetran integration supports better data governance. It helps ensure transparency, traceability, and SLA compliance across your data ecosystem.

How does data lineage support compliance with data privacy regulations?

Data lineage plays a key role in compliance monitoring by providing transparency into where data comes from, how it's processed, and where it ends up. This is crucial for meeting regulations like GDPR and HIPAA, and for maintaining strong data governance practices across the organization.

Why is data reliability more important than ever?

With more teams depending on data for everyday decisions, data reliability has become a top priority. It’s not just about infrastructure uptime anymore, but also about ensuring the data itself is accurate, fresh, and trustworthy. Tools for data quality monitoring and root cause analysis help teams catch issues early and maintain confidence in their analytics.

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