From the course: Data Pipeline Automation with GitHub Actions Using R and Python
Deployment with Docker - GitHub Tutorial
From the course: Data Pipeline Automation with GitHub Actions Using R and Python
Deployment with Docker
- [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 ahead and creating a new image or customizing the course image according to your needs. If you are new to Docker and may have additional requirements that not available in the current image, I recommend checking the official Python image for Python applications or the Docker project for our applications. In the next video, we'll learn how to create a workflow.
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