Peng precommit update and new notebooks

[pzdkn]

Description

We add two new notebooks to demonstrate how Squirrel can be combined with Spark to do the following tasks:

1. Notebook 11: Shows how we can load time-series data
2. Notebook 12: Shows how we can split data to be stored into different stores, depending on a categorical label.

Fixes # issue

Type of change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation update
• Refactoring including code style reformatting
• Other (please describe):

Checklist:

• I have read the contributing guideline doc (external contributors only)
• Lint and unit tests pass locally with my changes
• I have kept the PR small so that it can be easily reviewed
• I have made corresponding changes to the documentation
• I have added tests that prove my fix is effective or that my feature works
• All dependency changes have been reflected in the pip requirement files.

[AlirezaSohofi]

can we use zip_index here and avoid it_list = list(it)?

IterableSource(it).batched(N_SHARDS).zip_index(pad_length=9).map(lambda x: store.set(key=x[0], value=x[1])).join()

[pzdkn]

actually, the key can't be just an index but should be the smallest timestamp, but i'll come up with something to avoid the list call

[pzdkn]

I am not so sure why we are doing it. We have already sharded the data by partitioning it. The function is called for each partition/shard, then we need to somehow serialize the iterator to store it. So we kind of need the list call.

Except we want to further shard the data inside the function, but I am not sure what the additional benefit is?

[AlirezaSohofi]

We have already sharded the data by partitioning it.
My bad sorry, in that case yes we should call list().

[AlirezaSohofi]

LGTM 👍