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.

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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)

TEAMS

Reinforced Reliability

Built for Everyone

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?

sifflet a guide to assessing your need for a data observability

A Guide to Assessing your Need for Data Observability

Every data-driven team dreams of creating "fireworks" with their insights. But without control, the "fire" of the modern data stack can spark chaos.
Discover if you're fighting fires or igniting fireworks:

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Data Quality Monitoring is Dead

In this blog, we’ll explore why data quality monitoring falls short and how observability is reshaping the future of data management. Ready to make the shift?

Let’s dive in!

Data monitoring vs observability solutions

Data monitoring and data observability are often used interchangeably, but they serve distinct purposes in managing modern data systems. Understanding the difference is key to choosing the right solution for your organization.

Ready to clarify?

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

Frequently asked questions

Is Sifflet suitable for non-technical users who want to contribute to data quality?

Yes, and that’s one of the things we’re most excited about! Sifflet empowers non-technical users to define custom monitoring rules and participate in data quality efforts without needing to write dbt code. It’s all part of building a culture of shared responsibility around data governance and observability.

Why does great design matter in data observability platforms?

Great design is essential in data observability platforms because it helps users navigate complex workflows with ease and confidence. At Sifflet, we believe that combining intuitive UX with a visually consistent UI empowers Data Engineers and Analysts to monitor data quality, detect anomalies, and ensure SLA compliance more efficiently.

Why should companies invest in data pipeline monitoring?

Data pipeline monitoring helps teams stay on top of ingestion latency, schema changes, and unexpected drops in data freshness. Without it, issues can go unnoticed and lead to broken dashboards or faulty decisions. With tools like Sifflet, you can set up real-time alerts and reduce downtime through proactive monitoring.

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 Insights help improve data quality in my BI dashboards?

Sifflet Insights integrates directly into your BI tools like Looker and Tableau, providing real-time alerts about upstream data quality issues. This ensures you always have accurate and reliable data for your reports, which is essential for maintaining data trust and improving data governance.

Why is data observability so important for modern data teams?

Great question! Data observability is essential because it gives teams full visibility into the health of their data pipelines. Without it, small issues can quickly snowball into major incidents, like broken dashboards or faulty machine learning models. At Sifflet, we help you catch problems early with real-time metrics and proactive monitoring, so your team can focus on creating insights, not putting out fires.

Is it hard to set up the Sifflet and ServiceNow integration?

Not at all! It only takes a few minutes to get started. Just follow our step-by-step integration guide, and you’ll be ready to connect your data observability alerts directly to ServiceNow in no time.

How does Sifflet support enterprises with data pipeline monitoring?

Sifflet provides a comprehensive observability platform that monitors the health of data pipelines through features like pipeline error alerting, data freshness checks, and ingestion latency tracking. This helps teams identify issues early and maintain SLA compliance across their data workflows.

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Still have questions?