DatasetCatalog Class | FeatureStore | Teradata Package for Python - DatasetCatalog Class - Teradata Package for Python

Use the DatasetCatalog class to manage datasets within the Enterprise Feature Store. It provides functionality to create, list, retrieve, archive, and delete datasets, as well as manage dataset-related operations.

• DatasetCatalog Properties
• DatasetCatalog Methods

Syntax

DatasetCatalog(repo, data_domain=None)

Required Parameter

repo

Specifies the name of the database where the feature store is set up.

Optional Parameter

data_domain

Specifies the name of the data domain to refer for managing datasets. If not specified, then default database is used as the data domain.

Example setup

Upload features.

>>> from teradataml import load_example_data, FeatureProcess
>>> load_example_data('dataframe', 'sales')
>>> df = DataFrame("sales")

Create a feature store.

>>> fs = FeatureStore(repo='vfs_v1', data_domain='sales')

repo vfs_v1 does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.

Set up the feature store for this repository.

>>> fs.setup()

True

Run FeatureProcess to ingest features.

>>> fp = FeatureProcess(repo='vfs_v1', data_domain='sales', object=df, entity='accounts',
...                     features=['Jan', 'Feb', 'Mar', 'Apr'])

Example: Create a DatasetCatalog instance

>>> from teradataml import DatasetCatalog
>>> dc = DatasetCatalog(repo='vfs_v1', data_domain='sales')