What you'll learn

Data teams often operate with limited visibility into their pipelines, discovering failures only after dashboards break, stakeholders complain, or downstream systems behave unpredictably. Without strong monitoring and observability, issues remain hidden, root causes stay unclear, and data reliability suffers. In this course, DataOps Foundations: Monitoring and Observability, you’ll gain the ability to build transparent, trustworthy, and proactively monitored data systems that detect issues early and support confident data operations. First, you’ll explore the role of monitoring and observability, including how logs, metrics, traces, and data quality signals reveal pipeline health and system behavior. Next, you’ll discover how to apply monitoring practices across ingestion, transformation, and delivery by using thresholds, alerts, SLAs/SLOs, and anomaly detection to identify and respond to failures before they impact users. Finally, you’ll learn how to implement observability‑driven feedback loops that support root‑cause analysis, remediation, capacity planning, and continuous operational improvement. When you’re finished with this course, you’ll have the skills and knowledge of monitoring and observability needed to create resilient, visible, and dependable data pipelines that strengthen stakeholder trust and reduce operational firefighting.