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Holistic data observability across your organisation

Data you can finally rely upon

Illustration of technical and business users in an organization having a great data experience with Sifflet from beginning to end
Explore the capabilities

Strengthening Data Serenity

Catalog, Monitor and Validate all your Data Assets in One Place

TRACEABLE

Get a bird's-eye view of your data

Gain a clear view of your data with a platform that integrates with your data stack, provides centralized documentation, lineage, and searchable metadata.

Learn moreTechnical diagram of your full data stack with Sifflet
AcceSsible

Improve productivity and collaboration between engineers and data consumers

For everyone, working with and finding data becomes intuitive with a simple and automated UI, data discovery is simplified with a data catalog, and it is easy to connect with coding workflows.

Learn moreSifflet product screen of Data Catalog
Reliable

Data quality management simplified

Ensure reliable data quality with smart monitoring and alerting (without the noise), root cause analysis, 50+ data quality templates, and mapping capabilities.

Learn moreSifflet product screen of Monitoring and Alerting
CUSTOMER STORIES

"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.

Laurent Tachet des Combes
Head of Data, Meero
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"Sifflet allows you to find and trust your data"

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.

Marco Kleine-Böhme
VP Data & Analytics, jobvalley
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"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
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FAQs

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