Tutorial: Workflow for Collaborative Data Updates Using DVC and GitHub¶

Introduction¶

Learning Objectives

Master the workflow for managing collaborative data updates in projects using Data Version Control (DVC) and GitHub. This tutorial is designed to provide a clear guide for teams to maintain consistency and synchronization in their data-driven projects.

Prerequisites¶

Basic knowledge of DVC and GitHub.
A project set up with DVC and connected to a GitHub repository.

Collaborative Update Scenario¶

Imagine a collaborative environment where dataset updates need to be synchronized across a team's local environments.

Step-by-Step Workflow Guide¶

1. Local Data Update¶

Updating Data Locally

Action: Make changes to your dataset locally, such as adding or editing data.

2. Tracking Changes with DVC¶

Using DVC for Tracking

Command: Run dvc add <file_or_directory> to track changes.
```
dvc add data/dataset.csv
```
Result: The .dvc file is updated to reflect the new dataset state.

3. Committing and Pushing to GitHub¶

Syncing with GitHub

Commit and Push: Update your Git repository with the new data version.

git add data/dataset.csv.dvc
git commit -m "Update dataset.csv with new data"
git push

Outcome: Changes are now in the GitHub repository.

4. Team Syncing Process¶

Team Members' Actions

Git Pull: Team members pull the latest changes.
```
git pull
```
DVC Pull: Synchronize the local data with the updated version in DVC remote storage.
```
dvc pull
```
Consistency Achieved: Everyone works with the same data version.

Sync Mechanism Explained¶

:octicons-cloud-upload-24: DVC Remote Storage: Stores the updated data, accessible to all team members.
Local Data Sync: dvc pull ensures local data matches the remote version.

Ensuring Data Consistency¶

Version Control with .dvc Files: Crucial for indicating the current data version.
Data Synchronization: Managed through DVC commands, while Git handles .dvc file version control.

Conclusion¶

Key Takeaway

Following this workflow allows teams to efficiently collaborate on data-driven projects, ensuring data consistency, reproducibility, and effective teamwork. It's an essential practice for maintaining project integrity and collaborative efficiency.