Push samples to dataset on hub without having the dataset locally
Hi ! For now I would recommend creating a new Parquet file using `dataset_new.to_parquet()` and upload it to HF using `huggingface_hub` every time you get a new batch of data. You can name the Parquet files `0000.parquet`, `0001.parquet`, etc.

Though maybe make sure to not upload one file per sample since that would be inefficient. You can buffer your data and upload when you have enough new samples for example This is excellent, thanks!
### Feature request

Say I have the following code:

```
from datasets import Dataset
import pandas as pd
new_data = {
    "column_1": ["value1", "value2"],
    "column_2": ["value3", "value4"],
}
df_new = pd.DataFrame(new_data)
dataset_new = Dataset.from_pandas(df_new)
# add these samples to a remote dataset
```
It would be great to have a way to push dataset_new to a remote dataset that respects the same schema. This way one would not have to do the following:
```
from datasets import load_dataset
dataset = load_dataset('username/dataset_name', use_auth_token='your_hf_token_here')
updated_dataset = dataset['train'].concatenate(dataset_new)
updated_dataset.push_to_hub('username/dataset_name', use_auth_token='your_hf_token_here')
```





### Motivation

No need to download the dataset.

### Your contribution

Maybe this feature already exists, didnt see it though. I do not have the expertise to do this.