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Frequently asked questions

Can non-technical users benefit from Sifflet’s Data Catalog?
Yes, definitely! Sifflet is designed to be user-friendly for both technical and business users. With features like AI-driven description recommendations and easy-to-navigate asset pages, even non-technical users can confidently explore and understand the data they need.
What role does Sifflet’s Data Catalog play in data governance?
Sifflet’s Data Catalog supports data governance by surfacing labels and tags, enabling classification of data assets, and linking business glossary terms for standardized definitions. This structured approach helps maintain compliance, manage costs, and ensure sensitive data is handled responsibly.
What practical steps can companies take to build a data-driven culture?
To build a data-driven culture, start by investing in data literacy, aligning goals across teams, and adopting observability tools that support proactive monitoring. Platforms with features like metrics collection, telemetry instrumentation, and real-time alerts can help ensure data reliability and build trust in your analytics.
How does data observability fit into the modern data stack?
Data observability integrates across your existing data stack, from ingestion tools like Airflow and AWS Glue to storage solutions like Snowflake and Redshift. It acts as a monitoring layer that provides real-time insights and alerts across each stage, helping teams maintain pipeline health and ensure data freshness checks are always in place.
How does reverse ETL improve data reliability and reduce manual data requests?
Reverse ETL automates the syncing of data from your warehouse to business apps, helping reduce the number of manual data requests across teams. This improves data reliability by ensuring consistent, up-to-date information is available where it’s needed most, while also supporting SLA compliance and data automation efforts.
Can data observability support better demand forecasting for retailers?
Absolutely. By integrating historical sales, real-time transactions, and external data sources like weather or social trends, data observability platforms enhance forecast accuracy. They use machine learning to evaluate and adjust predictions, helping retailers align inventory with actual consumer demand more effectively.
What impact did Sifflet have on fostering a data-driven culture at Meero?
Sifflet’s intuitive UI and real-time data observability dashboards empowered even non-technical users at Meero to understand data health. This transparency helped build trust in data and promoted a stronger data-driven culture across the organization.
Is there a networking opportunity with the Sifflet team at Big Data Paris?
Yes, we’re hosting an exclusive after-party at our booth on October 15! Come join us for great conversations, a champagne toast, and a chance to connect with data leaders who care about data governance, pipeline health, and building resilient systems.
Still have questions?