Sifflet is ISO 27001 certified and SOC 2 compliant, and we use a separate secret manager to handle credentials securely. This setup ensures a strong audit trail and tight access control, making compliance monitoring and audit logging seamless for your data teams.

Data observability plays a key role in SLA compliance by detecting issues like ingestion latency, schema changes, or data drift before they impact downstream users. With proper data quality monitoring and real-time metrics, you can catch problems early and keep your dashboards and reports reliable.

Data observability complements data governance by continuously monitoring data pipelines for issues like data drift, freshness problems, or anomalies. With an observability platform like Sifflet, teams can proactively detect and resolve data quality issues, enforce data validation rules, and gain visibility into pipeline health. This real-time insight helps governance policies work in practice, not just on paper.

Great question! As AI and real-time data products become more widespread, data observability is crucial for ensuring data reliability, privacy, and performance. A strong observability platform helps reduce data chaos by monitoring pipeline health, identifying anomalies, and maintaining SLA compliance across increasingly complex data ecosystems.

Batch ingestion processes data in chunks at scheduled intervals, making it ideal for non-urgent tasks like overnight reporting. Real-time ingestion, on the other hand, handles streaming data as it arrives, which is perfect for use cases like fraud detection or live dashboards. If you're focused on streaming data monitoring or real-time alerts, real-time ingestion is the way to go.

Standardization is key to maintaining control as your dbt project grows. Sifflet supports this by centralizing metadata and enabling compliance monitoring through features like data contracts enforcement and asset tagging. This ensures consistency, improves data governance, and reduces the risk of data drift or unmonitored critical assets.

SQL Table Tracer uses a Monoid-based design to handle complex SQL structures like Common Table Expressions (CTEs) and subqueries. This approach allows it to incrementally and safely compose lineage information, ensuring accurate root cause analysis and data drift detection.

Sifflet simplifies data volume monitoring with plug-and-play integrations, AI-powered baselining, and unified observability dashboards. It automatically detects anomalies, connects them to business impact, and provides real-time alerts. Whether you're using Snowflake, BigQuery, or Kafka, Sifflet helps you stay ahead of data reliability issues with proactive monitoring and alerting.