Agentic observability is our vision for the future — where observability platforms don’t just monitor, they act. Think of it as moving from real-time alerts to intelligent copilots. With features like auto-remediation, dynamic thresholding, and incident response automation, Sifflet is building systems that can detect issues, assess impact, and even resolve known problems on their own. It’s a huge step toward self-healing pipelines and truly proactive data operations.

Sifflet supports Etam by embedding data governance into their workflows through automated monitoring, anomaly detection, and data lineage tracking. This gives the team better visibility into their data pipelines and helps them troubleshoot issues quickly without slowing down innovation.

The right technology can make a big difference. Adopting observability tools that offer features like data lineage tracking, data freshness checks, and pipeline health dashboards can reduce manual firefighting and help your team work more autonomously. This not only improves productivity but also makes day-to-day work more enjoyable.

Data quality monitoring is essential for maintaining the integrity of your data assets. It helps catch issues like missing values, inconsistencies, and outdated information before they impact business decisions. Combined with data observability, it ensures that your data catalog reflects trustworthy, high-quality data across the pipeline.

To prevent disruptions, it's essential to use data lineage tracking. This gives you visibility into how data flows through your systems, so you can assess downstream impacts before making changes. It’s a key part of data pipeline monitoring and helps maintain trust in your analytics.

Absolutely! Sifflet’s observability tools are fully compatible with Google BigQuery, so you can perform data quality monitoring, data lineage tracking, and anomaly detection right where your data lives.

Absolutely! With Sifflet's observability platform, you can view the health status of your Firebolt tables in real time. This allows for proactive data pipeline monitoring and helps ensure SLA compliance across your analytics workflows.

Using WHERE instead of HAVING when not working with GROUP BY clauses is crucial because WHERE filters data earlier in the query execution. This reduces the amount of data processed, which improves query speed and supports better metrics collection in your observability platform.