This repository is meant to be a guide to learning git, primarily via the Git Immersion tour. I've begun to intersperse my own experiences and lessons-learned.
This guide is meant to help data analysts or new analytics engineers get comfortable with git, the command line interface (CLI), and the principles behind version-control.
If you're a seasoned engineer -- welcome! This isn't for you. If you can name an alternative to git, chances are this isn't for you. There are better resources out there for y'all with more than a cursory knowledge of software engineering best-practices.
If you've been struggle to understand; you're coming from a non-traditional or business background into data; or you're at all like me -- welcome.
I hope this helps you as much as Git Immersion helped me.
I am not the author(s) nor maintainers of the two tutorials linked below.
I, humbly, am still learning software engineering best practices. I've gotten a lot more comfortable with Git, Bash, and the CLI since I first drafted these notes. My own first run through the tutorial is now years -- not months -- old.
The intent in this guide is to share resouces that I found extremely valuable in my analytics & data engineering journey, and to help address some of the challenges I ran into when working through these.
- Complete Ruby in Twenty Minutes
- Go to Git Immersion
- Follow those instructions. They will serve you well.
- As you work through the Git Immersion labs, consult these markdown files for notes.
- Bash for Beginners - Microsoft Developer YT course
- Undoing a commit notes (reset head?)
- atomic commit notes
- finish rebase vs merge notes, link Primeagen video(s)
- local vs remote branches
- why use a branch?
- pull vs fetch
- merge conflicts