Great question! With our latest dbt integration update, we’ve unified dbt models and the datasets they generate into a single asset. This means you get richer context and better visibility across your data pipelines, making it easier to track data lineage, monitor data quality, and ensure SLA compliance all from one place.

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.

When a data issue arises, Sifflet gives you the context you need to act fast. Our observability platform connects the dots across your data stack—tracking lineage, surfacing schema changes, and highlighting impacted assets. That makes root cause analysis much easier, whether you're dealing with ingestion latency or a failed transformation job. Plus, our AI helps explain anomalies in plain language.

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.

You can find the Sifflet team at Booth Y640 during Big Data LDN on September 18-19. Stop by to learn more about our data observability platform and how we’re helping organizations like the BBC and Penguin Random House improve their data reliability.

For Adaptavist, observability culture means going beyond tools. It's about clear ownership of alerts, integrating data quality monitoring into sprints, and giving stakeholders ways to provide feedback directly in dashboards. They even track observability metrics to continuously improve their own observability practices.

Look for platforms that offer end-to-end coverage including data freshness checks, anomaly detection, root cause analysis, and integrations with tools like Snowflake, Airflow, and dbt. The best observability tools also support collaboration, scalability, and proactive monitoring to keep your pipelines healthy and your data trustworthy.

With decentralized architectures, data pipeline monitoring becomes essential. Tools like Sifflet offer centralized visibility across domain-owned pipelines, helping teams stay aligned, detect anomalies, and ensure SLA compliance without slowing down local innovation.