Forge is our resolution agent that turns insights into actions. It recommends specific fixes based on past incidents, and with your approval, it can execute them automatically. Whether it’s retrying a dbt job or running a backfill, Forge reduces manual work and speeds up recovery. It’s a big step forward in incident response automation and keeping your data pipelines healthy.

Shippeo tracks critical operational data like order volume, GPS positions, and platform activity. With Sifflet, they monitor ingestion latency and data freshness to ensure that metrics powering dashboards and customer reports are always up to date.

Datadog is a strong choice for infrastructure and system observability, but it may not be the best fit for teams focused on data reliability and data governance. While it offers some data quality monitoring through Metaplane, it lacks the business context and advanced data lineage tracking needed to ensure trust in your analytics. For those priorities, a dedicated data observability platform like Sifflet is better equipped.

Diversity and innovation are core values at Sifflet. We believe that a diverse team brings a wider range of perspectives, which leads to more creative solutions in areas like cloud data observability and predictive analytics monitoring. Our culture encourages experimentation and continuous learning, making it a great place to grow.

Adaptavist built a platform health dashboard that scores each asset based on data freshness, quality, and reliability. This kind of data profiling helps them prioritize fixes, improve root cause analysis, and ensure continued trust in their analytics pipeline observability.

Data lineage tracking is essential when your dbt projects grow in size and complexity. Sifflet provides a unified, metadata-rich lineage graph that spans your entire data stack, helping you quickly perform root cause analysis and impact assessments. This visibility is crucial for maintaining trust and transparency in your data pipelines.

Sifflet supports data governance by allowing you to classify assets with tags and labels, define business terms in a shared glossary, and track data lineage. These features help ensure consistent definitions and safe handling of sensitive data across your stack.

Stakeholder trust is crucial because inconsistent or unreliable data can lead to poor decisions and reduced adoption of data-driven practices. You can protect this trust with strong data quality monitoring, real-time metrics, and consistent reporting. Data observability tools help by alerting teams to issues before they impact dashboards or reports, ensuring transparency and reliability.