We're so excited to welcome you at Booth #D15 on October 15 and 16! You’ll get to experience live demos of our latest data observability features, hear real client stories like Saint-Gobain’s, and explore how Sifflet helps improve data reliability and streamline data pipeline monitoring.

Sifflet’s observability platform offers proactive data pipeline monitoring through extensive monitoring tools, real-time alerts, and historical performance insights. These features help your team stay ahead of issues and ensure your data pipelines are always delivering high-quality, reliable data.

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.

When a data catalog integrates seamlessly with your databases, cloud storage, and data lakes, it enhances your ability to monitor data pipelines in real time. This connectivity supports better ingestion latency tracking and helps maintain a reliable observability platform.

Absolutely. By connecting data health to business workflows, business-aware observability enables more accurate SLA monitoring. Sifflet’s platform helps teams track service level indicators and proactively manage incidents before they breach SLAs, improving both reliability and accountability.

The ability to define joins directly in the monitor setup interface means you can now monitor relationships across datasets without writing custom SQL. This is crucial for data quality monitoring because many issues arise from inconsistencies between related tables. Now, you can catch those problems early and ensure better data reliability across your pipelines.

Traditional monitoring often relies on batch processing, leading to delays in inventory updates. It also struggles with data silos, lacks robust data quality monitoring, and is mostly reactive. In contrast, modern observability tools provide real-time insights, dynamic thresholding, and predictive analytics monitoring to keep up with fast-paced retail environments.

To assess AI health, track metrics like Mean Time to Detection (MTTD), Mean Time to Resolution (MTTR), and data freshness checks. These metrics, combined with robust data pipeline monitoring and anomaly scoring, give you a clear view into model performance and governance effectiveness over time.