Local Assistants
Accepted Talks:
Bridging the Gap Between DevOps and Data Professionals
Due to some unforeseen issues, the presenters will not be able to attend the conference in person. The plan is to run the event with remote presenters and local assistance. Programmers, regardless of their level of experience, enjoy solving increasingly complex challenges within their domain of expertise, and one of the main reasons they can spend more time working through such challenges is because of the automation recipes they have built around their workflows. Data Analysts, Engineers and Scientists automate the initial steps of inspecting, cleaning, and analysing new data sets while DevOps Engineers automate everything from the filesystem to the infrastructure of software products. These groups of (data and engineering) professionals are not too foreign to each other as they all speak the same language, Python. That said, the goal of this workshop is to bring the automation recipes from the data world into the DevOps world and vice-versa. In other words, to bring all the slang and word abbreviations both groups use -- in code -- and create a dialect that welcomes both, newcomers and experts to either field. In this workshop, we'll cover 4 major automation recipes from the Analytics and DevOps worlds to make you a more efficient and systematic programmer. Each section will last about 45-minutes, and the topics covered range from automating extract, transform, and load (ETL) pipelines to creating your own internal platform tools. By the end of the workshop, you will be able to speak some DevOps to your data professional colleagues, and some analytics to your engineering team (slang words included). The target audience for this session includes analysts of all levels, developers, data scientists and engineers wanting to learn automation and best practice recipes to increase their productivity with Python, and as programmers in general. The tutorial has a setup section, four major lessons of ~45 minutes each, and 3 breaks of 10 minutes each. In addition, each of the major sections contains some allotted time for exercises that are designed to help solidify the content taught throughout the workshop. See https://github.com/ramonpzg/pycon-za2022-analytics-devops for more details. Total time budgeted (including breaks) - 4 hoursImportant Note
Abstract
Audience
Format
Prerequisites (P) and Good To Have's (GTH)
Outline