Data observability gives you the visibility to detect freshness problems before they impact the business. By combining metrics like data age, expected vs. actual arrival time, and pipeline health dashboards, observability tools help teams catch delays early, trace where things broke down, and maintain trust in real-time metrics.

Traditional monitoring tools focus on infrastructure and application performance, while data observability digs into the health and trustworthiness of your data itself. At Sifflet, we combine metadata monitoring, data profiling, and log analysis to provide deep insights into pipeline health, data freshness checks, and anomaly detection. It's about ensuring your data is accurate, timely, and reliable across the entire stack.

Absolutely! While Sifflet’s monitors are typically scheduled, you can run them on demand using our API. This means you can integrate real-time data quality checks into your AI pipelines, ensuring your models are making decisions based on the freshest and most accurate data available. It's a powerful way to keep your AI systems responsive and reliable.

Sifflet enables real-time metrics and data freshness checks that surface anomalies before they escalate. With features like alerting, lineage tracking, and pre-prod validation, teams at Adaptavist can spot and fix problems early, reducing surprise outages and improving SLA compliance.

Sifflet makes root cause analysis easy by providing complete data lineage tracking for your Firebolt assets. You can trace issues back to their source, whether it's an upstream dbt model or a downstream Looker dashboard, all within a single platform.

Great question! The old playbook for data observability focused heavily on technical infrastructure and treated data like servers — if the pipeline ran and the schema looked fine, the data was assumed to be trustworthy. But today, data is a strategic asset that powers business decisions, AI models, and customer experiences. At Sifflet, we believe modern observability platforms must go beyond uptime and freshness checks to provide context-aware insights that reflect real business impact.

Flow Stopper enables early anomaly detection and integrates with your orchestrator to halt execution when issues are found. This makes it easier to perform root cause analysis before problems escalate and helps prevent incidents that could affect business-critical dashboards or KPIs.

Sifflet’s Freshness Monitor is designed to scale effortlessly. Thanks to our dynamic monitoring mode and continuous scan feature, you can monitor thousands of data assets without manually setting schedules. It’s a smart way to implement data pipeline monitoring across distributed systems and ensure SLA compliance at scale.