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.

Many teams are looking for more flexible and cost-efficient observability tools that offer better business user access and faster implementation. Monte Carlo, while a pioneer, has become known for its high costs, limited customization, and lack of business context in alerts. Newer platforms like Sifflet and Metaplane focus on real-time metrics, cross-functional collaboration, and easier setup, making them more appealing for modern data teams.

Reverse ETL is a game-changer for operational analytics. It moves data from your warehouse back into business tools like CRMs or marketing platforms. This enables teams across the organization to act on insights directly from the data warehouse. It’s a perfect example of how data integration has evolved to support autonomy and real-time metrics in decision-making.

Yes! While it currently supports GitHub and GitLab, Sifflet is actively working on bringing dbt Impact Analysis to Bitbucket. This expansion ensures broader coverage and supports more teams in achieving better data governance and observability.

Data quality monitoring ensures your data is accurate, complete, and consistent. A good data catalog should include profiling and validation tools that help teams assess data quality, which is crucial for maintaining SLA compliance and enabling proactive monitoring.

The Sifflet AI Assistant enhances data observability by automatically fine-tuning your monitoring setup using machine learning and dynamic thresholds. It continuously adapts to changes in your data pipelines, reducing false positives and ensuring accurate anomaly detection, even as your data scales globally.

Etam’s data strategy is built on clear business alignment, strong data quality monitoring, and a focus on delivering ROI across short, mid, and long-term horizons. With the help of an observability platform, they can adapt quickly, maintain data reliability, and support strategic decision-making even in uncertain conditions.

Sifflet’s AI agent, Sage, performs root cause analysis by combining metadata, query logs, code changes, and historical incidents to build a full narrative of the issue. This speeds up resolution and provides context-rich insights, making it easier to pinpoint and fix data pipeline issues efficiently.