Sifflet’s data catalog provided a clear and organized view of jobvalley’s data assets, making it much easier for new team members to understand the data landscape. This significantly cut down onboarding time and helped new hires become productive faster.

Real-time alerts are crucial for staying on top of potential issues before they escalate. By setting up threshold-based alerts and receiving notifications through channels like Slack or email, teams can act quickly to resolve problems. This proactive approach helps maintain SLA compliance and keeps your data operations running smoothly.

For sure! Our CEO, Salma Bakouk, will be speaking about the top data trends to watch in 2025, including how GenAI and advanced anomaly detection are shaping the future of observability platforms. You’ll walk away with actionable insights for your data strategy.

Combining data catalogs with data observability tools creates a holistic approach to managing data assets. While catalogs help users discover and understand data, observability tools ensure that data is accurate, timely, and reliable. This integration supports better decision-making, improves data reliability, and strengthens overall data governance.

We’re focused on solving the next generation of challenges, like hybrid environments, end-to-end data lineage tracking, and scaling data trust. Whether it's batch data observability or real-time pipeline monitoring, our mission is to help organizations build resilient, transparent, and future-proof data stacks.

By integrating real-time data quality monitoring directly into your orchestration layer, Flow Stopper gives Data Engineers the ability to stop the flow when something looks off. This means fewer broken pipelines, better SLA compliance, and more time spent on innovation instead of firefighting.

MCP helps LLMs become active participants in data quality monitoring by giving them access to structured resources like schema definitions, data validation rules, and profiling metrics. At Sifflet, we use this to detect anomalies, enforce data contracts, and ensure SLA compliance more effectively.

Full Data Stack Observability means having complete visibility into every layer of your data pipeline, from ingestion to business intelligence tools. At Sifflet, our observability platform collects signals across your entire stack, enabling anomaly detection, data lineage tracking, and real-time metrics collection. This approach helps teams ensure data reliability and reduce time spent firefighting issues.