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

0. Introduction

01 - What is Docker, and how does it help
02 - Course topics
03 - Prerequisites

1. Introducing Docker

04 - Terms and concepts
05 - Drawbacks of application development on bare metal and VMs
06 - Introducing containers
07 - Introducing Docker
08 - Introducing images
09 - Building and accessing images
10 - Microservices
11 - Containers and kernels

2. Working with Docker

12 - Installing Docker Desktop on Windows
13 - Installing Docker Desktop on macOS
14 - Exploring Docker Hub
15 - Running your first container using docker run
16 - Pulling images using docker pull
17 - Viewing a running container on Docker Desktop
18 - Removing containers and images

3. Building Images and Running Containers

19 - Building your first Docker image
20 - Overriding the CMD instruction
21 - Using the Python base image
22 - Running a container in interactive mode
23 - Specifying the Dockerfile to run a Python application
24 - Running the containerized Python application
25 - Running multiple containers from the same image
26 - Building an image that runs a setup script
27 - Overriding the ENTRYPOINT directive

4. Pushing Images to and Pulling Images from Docker Hub

28 - Pushing images to Docker Hub
29 - Pushing images to Docker Hub using Docker Desktop
30 - Pulling custom images from Docker Hub

5. Deploying a Containerized Model to Azure Container Instances

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

Conclusion

39 - Summary and next steps

Docker for Data Engineers

Authors

Janani Ravi