Lightning Talk: Using Jupyter Notebooks To Gain Insight Of Your Cluster

For the last 7 years or so Jupyter Notebooks (formerly known as IPython) has revolutionized the way scholars, enthusiasts and data analysts look at, process and work with data. On the Kubernetes side, …

For the last 7 years or so Jupyter Notebooks (formerly known as IPython) has revolutionized the way scholars, enthusiasts and data analysts look at, process and work with data. On the Kubernetes side, component (api-server, scheduler, controller manager, etc.) logs, events, etc. can be an indomitable fire hose of data that can rather difficult to analyze and work with in its raw form. However, as we’ll demonstrate, with the right tools, you can create data pipelines so that it can observed, analyzed and visualized in an interactive way using Jupyter notebooks. Insight therein can then be used for any task from performance tuning to debugging. The outline for this lightning talk is straightforward: * Problem statement (~ 1 min.) * Solution and tooling description (~45 sec.) * How the tooling was used ( ~1 min.) * Go over the data processing pipeline (~1 min.) * Results (~45 sec.)