The Importance of Virtual Environments for Data Engineers
Many data tools today are code based and built on top of python.
And while you don't have to be a python wizard what, it does mean is you'll need to get comfortable building and maintaining python style projects.
One critical best practice you'll need to understand is using virtual environments.
So in this video we'll talk about:
- What virtual environments are
- Why they're important as data engineers
- How you can create one to share with the rest of your team
Thank you for watching!
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Timestamps:
0:00 Intro
0:21 What are Virtual Environments?
1:25 Common Tools to Use
3:01 Create a Virtual Environment
6:12 Export & Share Dependencies
Title & Tags:
The Importance of Virtual Environments for Data Engineers
#kahandatasolutions #dataengineering #python
►► The Starter Guide for The Modern Data Stack (Free PDF)→ bit.ly/starter-mds Simplify “modern” architectures + better understand common tools & components
well done, thank you !
Wow, this video was incredibly helpful! I didn’t understand the concept of virtual environments until now 😊
Glad to hear it helped! Thanks for watching
Same here! So clear and helpful
Your content is always top notch. Very useful information.
Much appreciated!
super clear thanks!
Hi, Is it possible to have virtual env with different version of python without installing that particular version of python, for example I have python 3.11 installed and I would like to create venv with python 3.8 (without installing python 3.8 on my windows)?
I'm not sure (never tried to do this), but perhaps that's a good use case of PyEnv
He explained very well what is happening and how it is happening. but please, voice all your actions a little slower, because it is difficult for non-native English speakers to understand and we have to go back
First