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Frequently asked questions
How does Sifflet support data pipeline monitoring at Carrefour?
Sifflet enables comprehensive data pipeline monitoring through features like monitoring-as-code and seamless integration with data lineage tracking and governance tools. This gives Carrefour full visibility into their pipeline health and helps ensure SLA compliance.
How can I avoid breaking reports and dashboards during migration?
To prevent disruptions, it's essential to use data lineage tracking. This gives you visibility into how data flows through your systems, so you can assess downstream impacts before making changes. It’s a key part of data pipeline monitoring and helps maintain trust in your analytics.
How does Sifflet support data quality monitoring?
Sifflet makes data quality monitoring seamless with its auto-coverage feature. It automatically suggests fields to monitor and applies rules for freshness, uniqueness, and null values. This proactive monitoring helps maintain SLA compliance and keeps your data assets trustworthy and safe to use.
Can business users benefit from data observability too, or is it just for engineers?
Absolutely, business users benefit too! Sifflet's UI is built for both technical and non-technical teams. For example, our Chrome extension overlays on BI tools to show real-time metrics and data quality monitoring without needing to write SQL. It helps everyone from analysts to execs make decisions with confidence, knowing the data behind their dashboards is trustworthy.
How does Sifflet help improve data reliability for modern organizations?
At Sifflet, we provide a full-stack observability platform that gives teams complete visibility into their data pipelines. From data quality monitoring to root cause analysis and real-time anomaly detection, we help organizations ensure their data is accurate, timely, and trustworthy.
What kinds of metrics can retailers track with advanced observability tools?
Retailers can track a wide range of metrics such as inventory health, stock obsolescence risks, carrying costs, and dynamic safety stock levels. These observability dashboards offer time-series analysis and predictive insights that support better decision-making and improve overall data reliability.
What kind of data quality monitoring features does Sifflet Insights offer?
Sifflet Insights offers features like real-time alerts, incident tracking, and access to metadata through your Data Catalog. These capabilities support proactive data quality monitoring and streamline root cause analysis when issues arise.
What kind of monitoring should I set up after migrating to the cloud?
After migration, continuous data quality monitoring is a must. Set up real-time alerts for data freshness checks, schema changes, and ingestion latency. These observability tools help you catch issues early and keep your data pipelines running smoothly.