You’ll now find key dbt metadata like the last execution timestamp and status directly within the dataset catalog and asset pages. This makes real-time metrics and pipeline health monitoring more accessible and actionable across your observability platform.

Sifflet leverages machine learning to provide AI Suggestions for classification tags, helping teams automatically identify and label key data characteristics like PII or low cardinality. This not only streamlines data management but also enhances data quality monitoring by reducing manual effort and human error.

dbt Impact Analysis is a new feature from Sifflet that automatically comments on GitHub or GitLab pull requests with a list of impacted assets when a dbt model is changed. This helps teams enhance their data observability by understanding downstream effects before changes go live.

Looking ahead, Carrefour plans to expand monitoring to more than 1,500 tables, integrate AI-driven anomaly detection, and implement data contracts and SLA monitoring to further strengthen data governance and accountability.

Great question! When you treat data as a product, you're committing to delivering reliable, high-quality data to your consumers. Data observability ensures that issues like data drift, broken pipelines, or unexpected anomalies are caught early, so your data stays trustworthy and valuable. It's the foundation for data reliability and long-term success.

Data observability plays a key role by giving teams real-time visibility into the health of their data pipelines. With observability tools like Sifflet, you can monitor data freshness, detect anomalies, and trace issues back to their root cause. This allows you to catch and fix data quality issues before they impact business decisions, making your data more reliable and your operations more efficient.

An end-to-end data platform includes layers for ingestion, storage, transformation, orchestration, governance, observability, and analytics. Each part plays a role in making data reliable and actionable. For example, data lineage tracking and real-time metrics collection help ensure transparency and performance across the pipeline.

Great question! Data observability is essential because it gives teams full visibility into the health of their data pipelines. Without it, small issues can quickly snowball into major incidents, like broken dashboards or faulty machine learning models. At Sifflet, we help you catch problems early with real-time metrics and proactive monitoring, so your team can focus on creating insights, not putting out fires.