Sifflet

Sifflet detects a data quality issue through automated monitoring.

The system enriches the alert with lineage, downstream BI usage, and ownership information.

Engineers prioritize the incident based on business impact and investigate using centralized context.

Root cause is identified, and the issue is resolved, minimizing business disruption.

Implement Sifflet's data catalog to discover and document all data assets.

Establish data quality rules and policies to ensure compliance.

Monitor data quality metrics and track data lineage to maintain data integrity.

Automate data governance workflows to streamline data management processes.

Set up Sifflet to monitor data pipelines during the cloud migration process.

Establish data quality baselines before migration and compare them to post-migration metrics.

Automatically detect and alert on data quality regressions during migration.

Resolve data issues promptly to ensure a smooth and successful cloud migration.

Identify the data sources used for the data product.

Implement data quality checks and monitoring to ensure accuracy and completeness.

Track data lineage to maintain data integrity throughout the data product lifecycle.

Provide data quality reports and metrics to customers to build trust and confidence.

Implement data quality monitoring to ensure accuracy and completeness of data used in analytics.

Track data lineage to verify the origin and transformation of data used in analytics.

Establish data quality thresholds and alerts to detect anomalies.

Provide data quality reports to analytics teams to build confidence in analytics results.