Sifflet supports SLA compliance by offering intelligent alerting, dynamic thresholding, and real-time dashboards that track incident metrics and resolution times. Its data reliability dashboard gives teams visibility into SLA adherence and helps prioritize issues based on business impact, streamlining incident management workflows and reducing mean time to resolution.

Great question! Traditional data quality monitoring focuses on pre-defined rules and tests, but it often falls short when unexpected issues arise. Data observability, on the other hand, provides end-to-end visibility using telemetry instrumentation like metrics, metadata, and lineage. This makes it possible to detect anomalies in real time and troubleshoot issues faster, even in complex data environments.

Ensuring SLA compliance means keeping a close eye on metrics like throughput, resource utilization, and error rates. A robust observability platform can help you track these metrics in real time, so you stay within your service level objectives and keep stakeholders confident.

Data observability is the practice of monitoring data as it moves through your pipelines to detect, understand, and resolve issues proactively. It’s crucial because it helps data teams ensure data reliability, improve decision-making, and reduce the time spent firefighting data issues. With the growing complexity of data systems, having a robust observability platform is key to maintaining trust in your data.

Sifflet’s unified data catalog and observability features bring context-rich insights into your data workflows. This integration enhances data lineage tracking and supports stronger data governance by giving teams a holistic view of how data flows and transforms across your systems.

Data quality monitoring was key to jobvalley’s success. By using Sifflet’s data observability tools, they were able to validate the accuracy of business-critical tables, helping build trust in their data and supporting confident, data-driven decision-making.

Sifflet supports Etam by embedding data governance into their workflows through automated monitoring, anomaly detection, and data lineage tracking. This gives the team better visibility into their data pipelines and helps them troubleshoot issues quickly without slowing down innovation.

Great question! As more organizations rely on AI and analytics for decision-making, ensuring data quality, health, and reliability has become non-negotiable. Data observability platforms like Sifflet help teams detect issues early, reduce downtime, and maintain trust in their data pipelines.