Docker for Data Engineers

Course details

Unlock the potential of the popular containerization platform Docker to transform the way you develop, deploy, and manage applications. Instructor Janani Ravi shows you how Docker simplifies the creation and management of containerized applications and guides you through how to install Docker Desktop on Windows and macOS, manage images and containers, utilize the components of Docker Hub, and more. Along the way, get hands-on experience building Docker images, running containers in various modes, and deploying a containerized app with Azure Container Instances. Whether you're a developer, an IT professional, or just looking to streamline your apps, by the end of this course, you’ll be equipped with the skills you need to use Docker to enhance your applications' efficiency and predictability.

Concepts

Introduction

What is Docker, and how does it help
Course topics
Prerequisites

Introducing Docker

Terms and concepts
Drawbacks of application development on bare metal and VMs
Introducing containers
Introducing Docker
Introducing images
Building and accessing images
Microservices
Containers and kernels

Working with Docker

Installing Docker Desktop on Windows
Installing Docker Desktop on macOS
Exploring Docker Hub
Running your first container using docker run
Pulling images using docker pull
Viewing a running container on Docker Desktop
Removing containers and images

Building Images and Running Containers

Building your first Docker image
Overriding the CMD instruction
Using the Python base image
Running a container in interactive mode
Specifying the Dockerfile to run a Python application
Running the containerized Python application
Running multiple containers from the same image
Building an image that runs a setup script
Overriding the ENTRYPOINT directive

Pushing Images to and Pulling Images from Docker Hub

Pushing images to Docker Hub
Pushing images to Docker Hub using Docker Desktop
Pulling custom images from Docker Hub

Deploying a Containerized Model to Azure Container Instances

Azure Container Instances
Training an ML model and serializing to a pickle file
Setting up the Dockerfile for model prediction
Running a containerized app locally for churn prediction
Authenticating to Azure using the Azure CLI
Creating an Azure Container Registry and pushing the image
Deploying a container to Azure Container Instances
Making predictions using the containerized application

Conclusion

Summary and next steps