Tutorial: Managing Local Data Updates with DVC¶

Introduction¶

About this Tutorial

In this tutorial, you'll learn the practical steps to manage and track changes to datasets locally using Data Version Control (DVC). We'll cover a real-world scenario of updating a dataset and tracking these changes with DVC.

Prerequisites¶

Basic understanding of version control with Git.
DVC installed and initialized in your project.

Scenario: Updating Your Dataset¶

Imagine you've made updates to a dataset file named dataset.csv that's already being tracked by DVC. Your task now is to manage these updates efficiently.

Step-by-Step Guide to Manage Local Data Updates¶

1. Detecting Changes with DVC¶

How DVC Detects Changes

Checksum Mechanism: DVC uses checksums (hash values) to track file versions. A change in the file leads to a change in its checksum, signaling an update.

2. Updating the Data Version¶

Tracking the Updated File¶

Run the DVC Add Command:
```
dvc add dataset.csv
```
This command updates the .dvc file with the new checksum for dataset.csv.

Commit the Changes in Git:

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

Push the Updated Data:
```
dvc push
```
This step uploads the new version to your remote DVC storage.

Understanding the Purpose of This Process¶

Key Benefits

Reproducibility: Ensures stages of your project can be reproduced with the exact data versions.
Collaboration: Maintains consistency in data across team environments.
Traceability: Allows tracking of data changes throughout the project's history.

Conclusion¶

Wrapping Up

You've learned how DVC's integration with Git facilitates effective data versioning, ensuring consistent dataset management and traceable changes across your team. While focused on local data updates, remember that cloud-based updates might follow a slightly different process.