3. Register Entities

Registering entities represented in Credit Default dataset¶

In FeatureByte, an entity models real-world objects and ideas. These entities often correspond to keys in database tables.

Taking our loan applications scenario as an example, we can view "Client", "New Application", "Prior Application", and "Loan" as entities.

To help FeatureByte identify these entities in the data and the columns that represent them, we'll be creating and tagging these entities in this tutorial.

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import featurebyte as fb

# Set your profile to the tutorial environment
fb.use_profile("tutorial")

catalog_name = "Credit Default Dataset SDK Tutorial"
catalog = fb.Catalog.activate(catalog_name)
import featurebyte as fb

# Set your profile to the tutorial environment
fb.use_profile("tutorial")

catalog_name = "Credit Default Dataset SDK Tutorial"
catalog = fb.Catalog.activate(catalog_name)

16:58:25 | INFO     | SDK version: 3.2.0.dev66
INFO    :featurebyte:SDK version: 3.2.0.dev66
16:58:25 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
16:58:25 | INFO     | Using profile: staging
INFO    :featurebyte:Using profile: staging
16:58:25 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
INFO    :featurebyte:Using configuration file at: /Users/gxav/.featurebyte/config.yaml
16:58:25 | INFO     | Active profile: staging (https://staging.featurebyte.com/api/v1)
INFO    :featurebyte:Active profile: staging (https://staging.featurebyte.com/api/v1)
16:58:25 | INFO     | SDK version: 3.2.0.dev66
INFO    :featurebyte:SDK version: 3.2.0.dev66
16:58:25 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
16:58:25 | INFO     | Catalog activated: Credit Default Dataset SDK Tutorial
INFO    :featurebyte.api.catalog:Catalog activated: Credit Default Dataset SDK Tutorial
16:05:47 | WARNING  | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.
16:05:47 | INFO     | No catalog activated.
16:05:47 | INFO     | Catalog activated: Grocery Dataset Tutorial

Identify Entities in Your Data and Decide their Serving Name¶

When creating an entity, you'll need to define its serving name. This name acts as a unique identifier, particularly during preview or serving requests.

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catalog.create_entity(name="New Application", serving_names=["SK_ID_CURR"])
catalog.create_entity(name="Client", serving_names=["ClientID"])
catalog.create_entity(name="BureauReportedCredit", serving_names=["SK_ID_BUREAU"])
catalog.create_entity(name="PriorApplication", serving_names=["APPLICATION_ID"])
catalog.create_entity(name="Installment", serving_names=["INSTALMENT_ID"])
catalog.create_entity(name="Loan", serving_names=["LOAN_ID"])
catalog.create_entity(name="New Application", serving_names=["SK_ID_CURR"])
catalog.create_entity(name="Client", serving_names=["ClientID"])
catalog.create_entity(name="BureauReportedCredit", serving_names=["SK_ID_BUREAU"])
catalog.create_entity(name="PriorApplication", serving_names=["APPLICATION_ID"])
catalog.create_entity(name="Installment", serving_names=["INSTALMENT_ID"])
catalog.create_entity(name="Loan", serving_names=["LOAN_ID"])

Out[2]:

Entity

name	Loan
created_at	2025-10-15 08:58:26
updated_at	None
description	None
serving_names	['LOAN_ID']
catalog_name	Credit Default Dataset SDK Tutorial

Tag Columns Representing Entities¶

Now that we've established the entities, it's time to guide FeatureByte in mapping these entities to the actual data in our tables.

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application = catalog.get_table("NEW_APPLICATION")
application["ClientID"].as_entity("Client")
application["SK_ID_CURR"].as_entity("New Application")

client_profile = catalog.get_table("CLIENT_PROFILE")
client_profile["ClientID"].as_entity("Client")

bureau = catalog.get_table("BUREAU")
bureau["ClientID"].as_entity("Client")
bureau["SK_ID_BUREAU"].as_entity("BureauReportedCredit")

previous_application = catalog.get_table("PREVIOUS_APPLICATION")
previous_application["ClientID"].as_entity("Client")
previous_application["APPLICATION_ID"].as_entity("PriorApplication")

loan_status = catalog.get_table("LOAN_STATUS")
loan_status["LOAN_ID"].as_entity("Loan")
loan_status['APPLICATION_ID'].as_entity("PriorApplication")

installments_payments = catalog.get_table("INSTALLMENTS_PAYMENTS")
installments_payments["INSTALMENT_ID"].as_entity("Installment")
installments_payments["APPLICATION_ID"].as_entity("PriorApplication")

credit_card_balance = catalog.get_table("CREDIT_CARD_MONTHLY_BALANCE")
credit_card_balance["ClientID"].as_entity("Client")
application = catalog.get_table("NEW_APPLICATION")
application["ClientID"].as_entity("Client")
application["SK_ID_CURR"].as_entity("New Application")

client_profile = catalog.get_table("CLIENT_PROFILE")
client_profile["ClientID"].as_entity("Client")

bureau = catalog.get_table("BUREAU")
bureau["ClientID"].as_entity("Client")
bureau["SK_ID_BUREAU"].as_entity("BureauReportedCredit")

previous_application = catalog.get_table("PREVIOUS_APPLICATION")
previous_application["ClientID"].as_entity("Client")
previous_application["APPLICATION_ID"].as_entity("PriorApplication")

loan_status = catalog.get_table("LOAN_STATUS")
loan_status["LOAN_ID"].as_entity("Loan")
loan_status['APPLICATION_ID'].as_entity("PriorApplication")

installments_payments = catalog.get_table("INSTALLMENTS_PAYMENTS")
installments_payments["INSTALMENT_ID"].as_entity("Installment")
installments_payments["APPLICATION_ID"].as_entity("PriorApplication")

credit_card_balance = catalog.get_table("CREDIT_CARD_MONTHLY_BALANCE")
credit_card_balance["ClientID"].as_entity("Client")

Review Entities Relationships¶

Now, if we list the tables as we did in the previous tutorial, we'll notice that entities have been assigned to each table.

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display(catalog.list_tables())
display(catalog.list_tables())

	id	name	type	status	entities	created_at
0	68ef62086b88234729580944	CREDIT_CARD_MONTHLY_BALANCE	time_series_table	PUBLIC_DRAFT	[Client]	2025-10-15T08:57:44.900000
1	68ef62076b88234729580943	LOAN_STATUS	scd_table	PUBLIC_DRAFT	[Loan, PriorApplication]	2025-10-15T08:57:43.345000
2	68ef62056b88234729580942	PREVIOUS_APPLICATION	event_table	PUBLIC_DRAFT	[PriorApplication, Client]	2025-10-15T08:57:41.795000
3	68ef62016b88234729580941	INSTALLMENTS_PAYMENTS	event_table	PUBLIC_DRAFT	[Installment, PriorApplication]	2025-10-15T08:57:37.317000
4	68ef61ff6b88234729580940	BUREAU	event_table	PUBLIC_DRAFT	[Client, BureauReportedCredit]	2025-10-15T08:57:35.734000
5	68ef61fe6b8823472958093f	CLIENT_PROFILE	scd_table	PUBLIC_DRAFT	[Client]	2025-10-15T08:57:34.202000
6	68ef61fc6b8823472958093e	NEW_APPLICATION	dimension_table	PUBLIC_DRAFT	[New Application, Client]	2025-10-15T08:57:32.452000

We can also list entities separately:

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display(catalog.list_entities())
display(catalog.list_entities())

	id	name	serving_names	created_at
0	68ef62326bb594c3d5cc80b9	Loan	[LOAN_ID]	2025-10-15T08:58:26.443000
1	68ef62326bb594c3d5cc80b8	Installment	[INSTALMENT_ID]	2025-10-15T08:58:26.282000
2	68ef62316bb594c3d5cc80b7	PriorApplication	[APPLICATION_ID]	2025-10-15T08:58:26.128000
3	68ef62316bb594c3d5cc80b6	BureauReportedCredit	[SK_ID_BUREAU]	2025-10-15T08:58:25.966000
4	68ef62316bb594c3d5cc80b5	Client	[ClientID]	2025-10-15T08:58:25.803000
5	68ef62316bb594c3d5cc80b4	New Application	[SK_ID_CURR]	2025-10-15T08:58:25.635000

And let's examine the relationships between entities, which FeatureByte has conveniently outlined for us:

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display(catalog.list_relationships())
display(catalog.list_relationships())

	id	relationship_type	entity	related_entity	relation_table	relation_table_type	enabled	created_at	updated_at
0	68ef6235b5826853a69c9ca8	child_parent	Installment	PriorApplication	INSTALLMENTS_PAYMENTS	event_table	True	2025-10-15T08:58:29.854000	None
1	68ef62354cbe9385fb0bdb9a	child_parent	Loan	PriorApplication	LOAN_STATUS	scd_table	True	2025-10-15T08:58:29.302000	None
2	68ef6234cf8a902261e4385d	child_parent	PriorApplication	Client	PREVIOUS_APPLICATION	event_table	True	2025-10-15T08:58:28.715000	None
3	68ef62344cbe9385fb0bdb94	child_parent	BureauReportedCredit	Client	BUREAU	event_table	True	2025-10-15T08:58:28.148000	None
4	68ef6233b5826853a69c9ca2	child_parent	New Application	Client	NEW_APPLICATION	dimension_table	True	2025-10-15T08:58:27.202000	None

That's it for most important data modeling concepts in FeatureByte. In next tutorials we will look into concepts related to data cleaning and feature engineering.

For more details about concepts we discussed in this tutorial, feel free to explore:¶

More info on Entities