archive_entity() | FeatureStore Archive Method | Teradata Package for Python - archive_entity() - Teradata Package for Python

Use the archive_entity() method to archive an entity from the repository.

• An archived entity is not available for any further processing.
• An archived entity can be viewed using the list_entities(archived=True) method.

Required Parameter

entity

Specifies either the name of the entity or object of the entity to archive from the repository.

Example setup

>>> from teradataml import DataFrame, Entity, FeatureStore

Create teradataml DataFrame.

>>> load_example_data('dataframe', ['sales'])
>>> df = DataFrame("sales")

Create FeatureStore for repo 'vfs_v1'.

>>> fs = FeatureStore("vfs_v1")

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

Set up FeatureStore for this repository.

>>> fs.setup()

True

Example 1: Archive the Entity 'sales_data' in the repo 'vfs_v1' using Entity name

Create an entity using teradataml DataFrame Column.

>>> entity = Entity(name="sales_data", columns=df.accounts)

Apply the entity to FeatureStore.

>>> fs.apply(entity)

True

List all the available entities.

>>> fs.list_entities()

                       description               creation_time modified_time entity_column
name       data_domain                                                                   
sales_data ALICE              None  2025-07-28 04:54:34.687139          None      accounts

Archive Entity with name "sales_data".

>>> fs.archive_entity(entity=entity.name)

Entity 'sales_data' is archived.
True

List the entities after archive.

>>> fs.list_entities(archived=True)

         name data_domain description               creation_time modified_time               archived_time entity_column
0  sales_data       ALICE        None  2025-07-28 04:54:34.687139          None  2025-07-28 04:55:46.750000      accounts

Example 2: Archive the Entity 'sales_data' in the repo 'vfs_v1' using Entity object

Create an entity using teradataml DataFrame Column.

>>> entity2 = Entity(name="sales_data_df", columns=df.accounts)

Apply the entity to FeatureStore.

>>> fs.apply(entity2)

True

Archive Entity with Entity object.

>>> fs.archive_entity(entity=entity2)

Entity 'sales_data_df' is archived.
True

List the entities after archive.

>>> fs.list_entities(archived=True)

         name data_domain description               creation_time modified_time               archived_time entity_column
0  sales_data       ALICE        None  2025-07-28 04:54:34.687139          None  2025-07-28 04:55:46.750000      accounts
1  sales_data_df    ALICE        None  2025-07-28 04:56:01.123456          None  2025-07-28 04:57:35.456789      accounts