…late

The default template already demonstrates a Pandas mode on demand feature
view. Add transformed_conv_rate_python, the same transformation written
in native Python mode (mode="python"), so that feast init shows both
transformation modes side by side, and fetch its features in
test_workflow.py during both historical and online retrieval.

Part of feast-dev#5478

Signed-off-by: ryota717 <[email protected]>

…nline_store

feast apply on the local provider with the sqlite online store goes
through FeatureStore.plan() / _apply_diffs() rather than
FeatureStore.apply(). SqliteOnlineStore.plan() only produced tables for
feature views, stream feature views and label views, so an on demand
feature view with write_to_online_store=True was registered without a
backing table and the subsequent materialize / write_to_online_store
failed with 'sqlite3.OperationalError: no such table'.

Include such on demand feature views in plan(), mirroring what
update() already does for the non-plan apply path, and add unit tests.

Part of feast-dev#5478

Signed-off-by: ryota717 <[email protected]>

…late

Add transformed_conv_rate_on_write, an on demand feature view with
write_to_online_store=True that derives conv_rate_x_acc_rate and
expected_daily_conversions from driver_hourly_stats. Unlike the existing
on demand feature views, its transformation runs when data is written to
the online store (materialize / write_to_online_store) instead of on
every read.

test_workflow.py now shows the materialized values after
materialize_incremental, then writes a raw row through
write_to_online_store and reads the transformed values back.

Part of feast-dev#5478

Signed-off-by: ryota717 <[email protected]>

…l template

Extend test_repo_init so that, after feast apply and feast materialize on
a freshly initialized repo, the features of the write_to_online_store on
demand feature view can be read back from the online store. This covers
the full CLI path (plan-based apply on the local provider) end to end.

Part of feast-dev#5478

Signed-off-by: ryota717 <[email protected]>

Update the quickstart to include transformed_conv_rate_python and
transformed_conv_rate_on_write in the feature_definitions.py listing and
in the feast apply / materialize outputs, and add a step contrasting
read-time (Pandas / native Python) and write-time transformations.
Also list the on demand feature views in the template README.

Part of feast-dev#5478

Signed-off-by: ryota717 <[email protected]>