Quickly identify the cause of data quality issues
Streamline your data team’s troubleshooting workflow with automated data lineage and monitoring, eliminating hours of investigative work to pinpoint issues’ initial points of failure.
Proactive data quality management
- Decrease average time to detection by seamlessly deploying all monitors at every stage of the data lifecycle
- Decrease time to resolution with effortless identification of data quality issues’ origin through automated lineage and ownership definition
"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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