Скачать или смотреть Docker for Data Engineers

▬▬▬▬▬▬ T I M E S T A M P S ⏰ ▬▬▬▬▬▬

0:00 - Intro
0:58 - What is Docker
1:33 - VM vs Docker Use Case
2:03 - Docker Terminologies
4:50 - Tutorial of using Docker for Data Engineering
8:14 - Outro

In this video, we'll dive into the world of Docker and explore how it can be used by data engineers. Docker is an incredibly powerful tool for managing and deploying applications, and it has become an essential part of many data engineering workflows.

We'll start by covering the basics of Docker, including containers, images, and the Dockerfile.

Throughout the video, we'll provide practical examples and hands-on demos to help you gain a deeper understanding of how Docker can be used in a data engineering context. Whether you're new to Docker or looking to take your skills to the next level, this video is a must-watch for any data engineer.

Topics covered in this video include:

Introduction to Docker and containers
Creating and managing Docker images
Using the Dockerfile to build custom images
Running Docker containers for data engineering applications
Best practices for using Docker in a data engineering workflow
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**What is a container?**: You can Abstract containers as lightweight virtual machines with their own CPU, Memory and Network Resources
software that packages up code and all its dependencies
so the application runs quickly and reliably from one computing environment to another.
*Docker Image:* To start a container you need a docker image, this image is your blueprint for the container, you can get prebuilt images like MySQ
*Dockerfile:* A yaml based instructions for building the image. You can consider it like DNA to the image with all the set of instructions in a place to make the image.
**Dockerhub**: Cloud based GitHub for your Docker images; Building and hosting images for sharing across

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