Filter Large Dataset Entry by Entry
### Feature request

I am not sure if this is a new feature, but I wanted to post this problem here, and hear if others have ways of optimizing and speeding up this process.

Let's say I have a really large dataset that I cannot load into memory. At this point, I am only aware of `streaming=True` to load the dataset. Now, the dataset consists of many tables. Ideally, I would want to have some simple filtering criterion, such that I only see the "good" tables. Here is an example of what the code might look like:

```
dataset = load_dataset(
    "really-large-dataset",
    streaming=True
)
# And let's say we process the dataset bit by bit because we want intermediate results
dataset = islice(dataset, 10000)

# Define a function to filter the data
def filter_function(table):
    if some_condition:
        return True
    else:
        return False

# Use the filter function on your dataset
filtered_dataset = (ex for ex in dataset if filter_function(ex))
```

And then I work on the processed dataset, which would be magnitudes faster than working on the original. I would love to hear if the problem setup + solution makes sense to people, and if anyone has suggestions!

### Motivation

See description above

### Your contribution

Happy to make PR if this is a new feature