Add Dataset Factory Patterns to Experiment Tracking

[ravi-kumar-pilla]

Description

Resolves #1689

Development notes

• Discovering datasets defined via dataset factory pattern using datasets in pipelines before adding catalog and tracking datasets to kedro viz
• Used catalog._get_dataset(dataset_name, suggest=False) to discover patterns before setting catalog

NOTE:

Earlier we used catalog.exists() on the datasets for discovery which caused the below issues -

1. Users were not able to use Kedro Viz if the dataset defined in the catalog does not exist (This was resolved using exception handlers)
2. Users received a timed-out issue as the datasets resided remotely and in a distributed cluster
3. exists() also calls _get_dataset internally and also calls AbstractDataset.exists. We are removing this additional call

Example patterns -

QA notes

• Steps to Reproduce
• You can also follow the dataset factory patterns guide and modify the demo-project or create a spaceflights project
• After executing kedro run, you should see the factory pattern datasets in the experiment tracking run

Checklist

• [x] Read the contributing guidelines
• [x] Opened this PR as a 'Draft Pull Request' if it is work-in-progress
• [ ] Updated the documentation to reflect the code changes
• [x] Added new entries to the RELEASE.md file
• [x] Added tests to cover my changes

[ravi-kumar-pilla]

CircleCI Build fix - https://github.com/kedro-org/kedro-viz/pull/1819

[ravi-kumar-pilla]

Hi @iamelijahko ,

I know you are working on refactoring catalog api. I wanted to bring this to your notice if this helps -

At this moment we are using a private method of DataCatalog to get around resolving the dataset factory patterns in one of our use-case. catalog._get_dataset(dataset_name, suggest=False)

Thank you