From the course: Data Pipeline Automation with GitHub Actions Using R and Python

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Deployment with Docker

Deployment with Docker - GitHub Tutorial

From the course: Data Pipeline Automation with GitHub Actions Using R and Python

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Deployment with Docker

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- [Instructor] In the previous video, we reviewed the core functionality of GitHub actions. In this video, we will dive into more details about the motivation for deploying a workflow with Docker image. If I need to define the motivation for using a container for our deployment, in one word, it would be environment and in two words, reproducible environment. Docker may have a high learning curve, but it was worth the effort. when you deploy your code in a remote environment. It enables you to shift your code with the environment in which you developed and test the code with. Plus, it is an industry standout and its use case go beyond code deployment. During this course, we'll use the course image, which is rkrispin backslash data pipeline automation with GitHub actions with dash separator, with the tag of prod. The image Docker file and its supporting files can be found under the .dev container folder in the course repository. If you feel comfortable with Docker, I recommend going…

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