Absolutely! By tagging fields like 'Low Cardinality', data teams can quickly identify which fields are best suited for specific monitors. This enables more targeted data pipeline monitoring, making it easier to detect anomalies and maintain SLA compliance across your analytics pipeline.

SQL Table Tracer (STT) is a lightweight library that automatically extracts table-level lineage from SQL queries. It identifies both destination and upstream tables, making it easier to understand data dependencies and build reliable data lineage workflows. This is a key component of any effective data observability strategy.

You can use a mix of threshold-based alerts, machine learning for anomaly detection, and visual freshness indicators in your BI tools. Pair these with data lineage tracking and root cause analysis to catch and resolve issues quickly. A modern data observability platform like Sifflet makes it easy to set up proactive monitoring tailored to your business needs.

Absolutely! Sifflet’s user-friendly interface and clear data asset indicators make it easy for business users to find and trust the right data. With features like visual data discovery and real-time metrics, it bridges the gap between technical teams and business stakeholders.

We're excited to be investing in four key areas: enhancing our product roadmap, expanding our AI-powered capabilities, growing our North American presence, and accelerating hiring across teams. These efforts will help us continue leading in cloud data observability and better serve our growing customer base.

The growing complexity of data products, especially with AI and real-time use cases, is driving the need for centralized data observability platforms. These platforms support proactive monitoring, root cause analysis, and incident response automation, making it easier for teams to maintain data reliability and optimize resource utilization.

Sifflet uses AI-driven context and dynamic thresholding to prioritize alerts based on impact and relevance. Its intelligent alerting system ensures users only get notified when it truly matters, helping reduce alert fatigue and enabling faster, more focused incident response.

As Shippeo scaled, they faced silent data leaks, inconsistent metrics, and data quality issues that impacted billing and reporting. By adopting Sifflet, they gained visibility into their data pipelines and could proactively detect and fix problems before they reached end users.