Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics.
Exhaustive mapping of all dependencies between assets, from ingestion to BI.
Information-rich data catalog
Root cause analysis
Incident management reporting.
Sifflet seamlessly integrates into your data sources and preferred tools, and can run on AWS, Google Cloud Platform, and Microsoft Azure.
"In addition to monitoring the quality and accuracy of the data assets, we were also looking to ensure the reliability of the pipelines, observe schema changes and other metadata-related metrics. More than anything else, we wanted to know the root cause of each anomaly detected and conduct proper incident management reporting. Sifflet ticked all of our boxes."
In addition to monitoring the quality and accuracy of the data assets, we were also looking to ensure the reliability of the pipelines, observe schema changes and other metadata-related metrics.
We chose Sifflet for its wide offering. We’ve checked out other vendors from the space, and they have all data quality rules, but what we need is the next step. Being able to know what to do once that rule fails.
It is very convenient to have the data quality feature together with the data catalog. I think it’s a great combination: the data catalog allows you to find all the data you need and the data quality feature monitors our data flows, ensuring that the data we use is reliable at all times.