# Data Version Control VS Git

## Git: (Manages source code)

* Stores source code, configuration files etc
    
* Version controls
    
* Provides auditing feature.
    
* Provides Role based access control
    

Limitations:

* Git is not designed for Large data(GB,TB)
    
* Slows down when multiple people handles(push/pull) large files
    
* Cost Ineffective when it comes to large files
    

## DVC: (Manages datasets)

* Stores Data sets that are required to train the models
    
* Supports Large data sets.
    
* Comes with version control capabilities
    
* Cost effective
    
* Durable
    

## How to use DVC+Object store+GIT for managing data sets.

```plaintext
#Installation
pip install git
pip install dvc
pip install dvc-s3

#Initialization
git init
dvc init
dvc remote add -d NAME URI://NAME_OF_BUCKET 

#Adds the datasets to Objectstore
#Adds the metadata to GIT
dvc add sample.csv      # creats sample.csv.dvc 
git add sample.csv.dvc  # file with metadata and checksum           
git add .dvc            # .dvc/config specifies the location of Objectstore
git commit -m "storing dataset metadata and objectstore location in Git"

#push the changes
git push      # pushes metadata and objectstore information to GIT 
dvc push.     # pushes the data set to Objectstore
```

GIT metadata information is the source of truth for the latest datasets being used.