A Seriously Smart Upgrade.
Prevent, detect and resolve incidents faster than ever before. No matter what your data stack throws at you, your data quality will reach new levels of performance.
No More Over Reacting
Sifflet takes you from reactive to proactive, with real-time detection and alerts that help you to catch data disruptions, before they happen. Watch your mean time to detection fall rapidly. On even the most complex data stacks.
- Advanced capabilities such as multidimensional monitoring help you seize complex data quality issues, even before breaks
- ML-based monitors shield your most business-critical data, so essential KPIs are protected and you get notified before there is business impact
- OOTB and customizable monitors give you comprehensive, end-to-end coverage and AI helps them get smarter as they go, reducing your reactivity even more.
Resolutions in Record Time
Get to the root cause of incidents and resolve them in record time.
- Quickly understand the scope and impact of an incident thanks to detailed system visibility
- Trace data flow through your system, identify the start point of issues, and pinpoint downstream dependencies to enable a seamless experience for business users, all thanks to data lineage
- Halt the propagation of data quality anomalies with Sifflet’s Flow Stopper
"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."
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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.
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