Dok Talks #111 - Scheduled Scaling with Dask and Argo Workflows

Data on Kubernetes Community

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2y ago 1:05:30

M4A•Головна епізоду

https://go.dok.community/slack
https://dok.community/
ABSTRACT OF THE TALK
Complex computational workloads in Python are a common sight these days, especially in the context of processing large and complex datasets. Battle-hardened modules such as Numpy, Pandas, and Scikit-Learn can perform low-level tasks, while tools like Dask makes it easy to parallelize these workloads across distributed computational environments. Meanwhile, Argo Workflows offers a Kubernetes-native solution to provisioning cloud resources in Kubernetes and triggering workflows on a regular schedule. Being Kubernetes-native, Argo Workflows also meshes nicely with other Kubernetes tools. This talk discusses the combination of these two worlds by showcasing a set-up for Argo-managed workflows which schedule and automatically scale-out Dask-powered data pipelines in Python.
BIO
Former academic in the field of renewable energy simulation and energy systems analysis. Currently responsible for architecting and maintaining the cloud- and data strategy at ACCURE Battery Intelligence
KEY TAKE-AWAYS FROM THE TALK
Argo Workflows + Dask is a nice combination for data-processing pipelines. There are a a few "gotchyas" to be on the look-out for, but in nevertheless this is still a generally-applicable and powerful combination.
https://github.com/sevberg

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