An IT discipline for monitoring and correlating data points in order to optimize business, application, and/or IT performance. Sub-disciplines include: business observability, operations observability, data pipeline observability, model observability, and data quality observability.
Data observability is a measure that provides the continuous, holistic view of a data landscape needed for a streamlined DataOps implementation. This article explains observability and how it applies to data.
Data observability, or observability for short, proposes a systemic solution that takes a fresh approach compared with previous generations of application performance monitoring (APM), DataOps and ITOps. This blog defines observability and examines how key stakeholders can use it to meet business requirements for production analytics and AI workloads. It explains how data observability applies to three overlapping segments—on-premises data lakes, hybrid cloud and multi-cloud data warehouses—to help data analytics leaders understand how best to regain control of all those pipelines.
Observability seeks to help. This emerging discipline offers processes and tools that observe the health of the business, IT systems, and data on a real-time basis. It enables enterprises to monitor many indicators, correlate them, generate alerts, identify issues, assess root causes, and remediate issues. This means listening to lots of noise and extracting just the right signals. Observability comprises five sub-disciplines, each of which has its own product category: business, operations, data pipeline, model, and data quality.