DODAS enables the execution of user analysis code both in batch mode and interactively via the Jupyter interface. DODAS is highly customizable and offers several building blocks that can be combined to create the best service composition for a given use case. The currently available blocks allow combining Jupyter and HTCondor as well as Jupyter and Spark or simply a jupyter interface. In addition, they allow the management of data via caches to optimize the processing of remote data. This can be done either via XCache or MinIO S3 object storage capabilities. DODAS is based on docker containers and the related orchestration relies on Kubernetes enabling the possibility to compose the building blocks via a web-based user interface thanks to Kubeapps.
About this Workshop
In this tutorial the DODAS fundamentals will be presented and will be shown a live user oriented demo.
Target audience: This tutorial is designed for scientific communities, developers, and end users who want to set-up interactive analysis platforms integrated with existing batch systems.
This session is chaired by Daniele Spiga (IFNF) and Diego Ciangottini (IFNF).
