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.

Great question! If you're constantly dealing with missing values, duplicates, or inconsistent formats, a data observability platform can be a game-changer. It provides real-time metrics and data quality monitoring, so you can detect and fix issues before they impact your reports or decisions.

Sifflet's integration with Apache Airflow brings powerful data observability features directly into your orchestration workflows. It helps data teams monitor DAG run statuses, understand downstream dependencies, and apply data quality monitoring to catch issues early, ensuring data reliability across the stack.

A great data observability tool should offer automated data quality checks like data freshness checks and schema change detection, field-level data lineage tracking for root cause analysis, and a powerful metadata search engine. These capabilities streamline incident response and help maintain data governance across your entire stack.

Sifflet enables real-time metrics and data freshness checks that surface anomalies before they escalate. With features like alerting, lineage tracking, and pre-prod validation, teams at Adaptavist can spot and fix problems early, reducing surprise outages and improving SLA compliance.

Great question! MCP allows large language models to access structured external context like pipeline metadata, logs, and diagnostics tools. At Sifflet, we use MCP to enhance data observability by enabling intelligent agents to monitor, diagnose, and act on issues across complex data pipelines in real time.

Acceldata integrates cost observability into its platform, giving you predictive insights into cloud spend, storage usage, and compute performance. This proactive monitoring helps teams plan capacity better and avoid unexpected overages, making it a great choice for enterprises juggling both data reliability and budget constraints.

Great question! Data monitoring is like your car's dashboard—it alerts you when something goes wrong, like a failed pipeline or a missing dataset. Data observability, on the other hand, is like being the driver. It gives you a full understanding of how your data behaves, where it comes from, and how issues impact downstream systems. At Sifflet, we believe in going beyond alerts to deliver true data observability across your entire stack.