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.
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.
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
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
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
Built for Everyone
Sifflet’s monitoring features reinforce data reliability for all users, so business can deliver.
Looking for more?
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:
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?
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.
Data Reliability, Reinforced
Sifflet’s monitoring features reinforce data reliability for all users, so business can deliver.
"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. "
"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."
"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"
"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."
"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."
Frequently asked questions
Data-quality-as-code (DQaC) allows you to programmatically define and enforce data quality rules using code. This ensures consistency, scalability, and better integration with CI/CD pipelines. Read more here to find out how to leverage it within Sifflet
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
AI enhances data observability with advanced anomaly detection, predictive analytics, and automated root cause analysis. This helps teams identify and resolve issues faster while reducing manual effort. Have a look at how Sifflet is leveraging AI for better data observability here
Data observability ensures data governance policies are adhered to by tracking data usage, quality, and lineage. It provides the transparency needed for accountability and compliance. Read more here.
Yes! While smaller organizations may have fewer data pipelines, ensuring data quality and reliability is equally important for making accurate decisions and scaling effectively. What really matters is the data stack maturity and volume of data. Take our test here to find out if you really need data observability.
-p-500.png)
