At Sifflet, we’re leveraging MCP to build agents that can observe, decide, and act across distributed systems. By injecting telemetry data, user context, and pipeline metadata as structured resources, our agents can navigate complex environments and improve distributed systems observability in a scalable and modular way.

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 quality refers to how accurate, complete, consistent, and timely your data is. It's essential because poor data quality can lead to unreliable analytics, missed business opportunities, and even financial losses. Investing in data quality monitoring helps teams regain trust in their data and make confident, data-driven decisions.

Subdomains empower each team to manage their own observability setup, from configuring monitors to setting thresholds. This decentralization speeds up time-to-value and reduces the need for constant involvement from the central platform team, making self-service data observability a reality.

Data observability helps organizations ensure data quality, monitor pipelines in real time, and build trust in their data. At Big Data LDN, we’ll share how companies like Penguin Random House use observability tools to improve data governance and drive better decisions.

Absolutely! Sifflet supports real-time metrics and data quality monitoring across your pipelines. Our AI agents continuously track data freshness, schema changes, and anomalies, so you’re alerted the moment something looks off. It’s like having a 24/7 data guardian that ensures your business decisions are based on accurate and timely information.

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.

Data monitoring focuses on detecting issues like failed jobs or freshness violations, often after the fact. Data observability, on the other hand, provides real-time metrics, proactive alerts, and end-to-end visibility into your data pipelines. With Sifflet’s observability platform, you don’t just monitor—you understand, troubleshoot, and continuously improve your data operations.