Data observability is the practice of monitoring data pipelines, capturing and correlating events that characterize the performance of analytics workloads.
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Data observability seeks to simplify modern data pipelines by monitoring and correlating data workload events across the application, data, and infrastructure layers of the stack. Observability helps resolve the issues that break production analytics and AI workloads.
- Kevin Petrie in Slides: The Rise of Data Observability: Improving the Scale, Performance, and Accuracy of Modern Data Pipelines
March 10, 2021 (Speech)
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.
- Kevin Petrie in Data Observability: the Path to Healthier Analytics and AI Operations
December 23, 2020 (Blog)
Data observability is not a certain process or tool, but rather a measure of transparency and maturity of data components. Using observability as a central measure when building data pipelines can increase overall manageability of data landscapes. Accordingly, observability is complementary to DataOps and its goal of “a culture of continuous improvement”
- Julian Ereth in Data Observability - A Crucial Property in a DataOps World
July 2, 2018 (Blog)
Relevant Content
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