As data systems become increasingly complex and integral to business operations, issues related to data quality and reliability pose significant challenges, often requiring manual checks and ad hoc solutions that are inefficient and reactive. Current data observability tools tend to highlight only surface-level problems, lacking the context needed to trace these issues back to their origins within data pipelines. Datadog Data Observability aims to address these challenges by offering comprehensive visibility that spans the entire data life cycle, from ingestion to downstream usage. The tool enables real-time detection, diagnosis, and resolution of data issues by monitoring infrastructure elements like Kafka streams and Spark jobs, and performing checks for data quality metrics such as freshness and accuracy. By providing end-to-end observability, Datadog fosters better collaboration between data and software engineers, helping teams to quickly identify and address complex data issues, such as schema drift or resource underprovisioning, thereby maintaining trust in data systems and minimizing business disruptions. This is facilitated through built-in metrics and alerts, machine learning anomaly detection, and visibility into data lineage, allowing for a more proactive and coordinated approach to data management.