Expanding streaming capabilities
Related to: https://github.com/huggingface/datasets/issues/3444 Cool ! `filter` will be very useful. There can be a filter that you can apply on a streaming dataset:
```python
load_dataset(..., streaming=True).filter(lambda x: x["lang"] == "sw")
```

Otherwise if you want to apply a filter on the source files that are going to be used for streaming, the logic has to be impIemented directly in the dataset script, or if there's no dataset script this can be done with pattern matching
```python
load_dataset(..., lang="sw")                    # if the dataset script supports this parameter
load_dataset(..., data_files="data/lang=sw/*")  # if there's no dataset script, but only data files
```

--------------

Here are also some additional ideas of API to convert from iterable to map-style dataset:
```python
on_disk_dataset = streaming_dataset.to_disk()
on_disk_dataset = streaming_dataset.to_disk(path="path/to/my/dataset/dir")

in_memory_dataset = streaming_dataset.take(100).to_memory()  # to experiment without having to write files
```
--------------

Finally regarding `push_to_hub`, we can replace `batch_size` by `shard_size` (same API as for on-disk datasets). The default is 500MB per file

Let me know what you think ! Regarding conversion, I'd also ask for some kind of equivalent to `save_to_disk` for an `IterableDataset`.

Similarly to the streaming to hub idea, my use case would be to define a sequence of dataset transforms via `.map()`, using an `IterableDataset` as the input (so processing could start without doing whole download up-front), but streaming the resultant processed dataset just to disk. That makes sense @athewsey , thanks for the suggestion :)

Maybe instead of the `to_disk` we could simply have `save_to_disk` instead:
```python
streaming_dataset.save_to_disk("path/to/my/dataset/dir")
on_disk_dataset = load_from_disk("path/to/my/dataset/dir")

in_memory_dataset = Dataset.from_list(list(streaming_dataset.take(100)))  # to experiment without having to write files
```
Some ideas for a few features that could be useful when working with large datasets in streaming mode. 

## `filter` for `IterableDataset`
Adding filtering to streaming datasets would be useful in several scenarios:
- filter a dataset with many languages for a subset of languages
- filter a dataset for specific licenses
- other custom logic to get a subset
The only way to achieve this at the moment is I think through writing a custom loading script and implementing filters there.

## `IterableDataset` to `Dataset` conversion
In combination with the above filter a functionality to "play" the whole stream would be useful. The motivation is that often one might filter the dataset to get a manageable size for experimentation. In that case streaming mode is no longer necessary as the filtered dataset is small enough and it would be useful to be able to play through the whole stream to create a normal `Dataset` with all its benefits.

```python
ds = load_dataset("some_large_dataset", streaming=True) 
ds_filter = ds.filter(lambda x: x["lang"]="fr")
ds_filter = ds_filter.stream() # here the `IterableDataset` is converted to a `Dataset`
```
Naturally, this could be expanded with `stream(n=1000)` which creates a `Dataset` with the first `n` elements similar to `take`.

## Stream to the Hub
While streaming allows to use a dataset as is without saving the whole dataset on the local machine it is currently not possible to process a dataset and add it to the hub. The only way to do this is by downloading the full dataset and saving the processed dataset again before pushing them to the hub. The API could looks something like:

```python
ds = load_dataset("some_large_dataset", streaming=True)
ds_filter = ds.filter(some_filter_func)
ds_processed = ds_filter.map(some_processing_func)
ds_processed.push_to_hub("new_better_dataset", batch_size=100_000)
```

Under the hood this could be done by processing and aggregating `batch_size` elements and then pushing that batch as a single file to the hub. With this functionality one could process and create TB scale datasets while only requiring size of `batch_size` local disk space.

cc @lhoestq @albertvillanova 