We’ve supercharged our dbt integration! Sifflet now offers deeper metadata visibility and powerful dbt impact analysis for both GitHub and GitLab. This helps you assess the downstream effects of model changes before deployment, boosting your confidence and control in data pipeline monitoring.

One standout example is their ad inventory forecasting tool. By embedding real-time metrics into internal tools, sales teams can plan campaigns more precisely and avoid last-minute scrambles. It’s a great case of using data to improve both accuracy and efficiency.

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.

Absolutely! With shared visibility into data flows and transformations, observability platforms foster better communication between data engineers, analysts, and business users. Everyone can see what's happening in the pipeline, which encourages ownership and teamwork around data reliability.

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.

Bad data can lead to a range of issues including financial losses, poor strategic decisions, compliance risks, and reduced team productivity. Without proper data quality monitoring, companies may struggle with inaccurate reports, failed analytics, and even reputational damage. That’s why having strong data observability tools in place is so critical.

For smaller teams, simplicity and clarity are key. Implementing lightweight data observability dashboards and using tools that support real-time alerts and Slack notifications can help them stay agile without feeling overwhelmed. Also, defining clear roles and giving access to self-service tools boosts autonomy and satisfaction.

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.