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 columns in database tables.

Taking our credit default scenario as an example, we can view "Client", "New Application", "Prior Application", and "Consumer 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.

In [1]:

            
                Copied!
                
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)

16:38:04 | WARNING  | Service endpoint is inaccessible: http://featurebyte-server:8088/
16:38:04 | INFO     | Using profile: tutorial
16:38:04 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
16:38:04 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:38:04 | INFO     | SDK version: 2.1.0.dev113
16:38:04 | INFO     | No catalog activated.
16:38:04 | INFO     | 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.

In [2]:

            
                Copied!
                
catalog.create_entity(name="New Application", serving_names=["NEW_APPLICATION_ID"])
catalog.create_entity(name="Client", serving_names=["CLIENT_ID"])
catalog.create_entity(name="Prior Application", serving_names=["PRIOR_APPLICATION_ID"])
catalog.create_entity(name="Consumer Loan", serving_names=["CONSUMER_LOAN_ID"])
catalog.create_entity(name="New Application", serving_names=["NEW_APPLICATION_ID"])
catalog.create_entity(name="Client", serving_names=["CLIENT_ID"])
catalog.create_entity(name="Prior Application", serving_names=["PRIOR_APPLICATION_ID"])
catalog.create_entity(name="Consumer Loan", serving_names=["CONSUMER_LOAN_ID"])

Out[2]:

Entity

name	Consumer Loan
created_at	2025-03-01 08:38:06
updated_at	None
description	None
serving_names	['CONSUMER_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.

In [3]:

            
                Copied!
                
                    
                    
                
                

        
application = catalog.get_table("NEW_APPLICATION")
application["CLIENT_ID"].as_entity("Client")
application["APPLICATION_ID"].as_entity("New Application")

prior_applications = catalog.get_table("PRIOR_APPLICATIONS")
prior_applications["CLIENT_ID"].as_entity("Client")
prior_applications["APPLICATION_ID"].as_entity("Prior Application")

consumer_loans = catalog.get_table("CONSUMER_LOAN_STATUS")
consumer_loans["LOAN_ID"].as_entity("Consumer Loan")
consumer_loans["CLIENT_ID"].as_entity("Client")

consumer_installments = catalog.get_table("CONSUMER_INSTALLMENTS")
consumer_installments["LOAN_ID"].as_entity("Consumer Loan")
application = catalog.get_table("NEW_APPLICATION")
application["CLIENT_ID"].as_entity("Client")
application["APPLICATION_ID"].as_entity("New Application")

prior_applications = catalog.get_table("PRIOR_APPLICATIONS")
prior_applications["CLIENT_ID"].as_entity("Client")
prior_applications["APPLICATION_ID"].as_entity("Prior Application")

consumer_loans = catalog.get_table("CONSUMER_LOAN_STATUS")
consumer_loans["LOAN_ID"].as_entity("Consumer Loan")
consumer_loans["CLIENT_ID"].as_entity("Client")

consumer_installments = catalog.get_table("CONSUMER_INSTALLMENTS")
consumer_installments["LOAN_ID"].as_entity("Consumer Loan")

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.

In [4]:

            
                Copied!
                
display(catalog.list_tables())
display(catalog.list_tables())

	id	name	type	status	entities	created_at
0	67c2c752924afe7a79ec6f27	CONSUMER_INSTALLMENTS	time_series_table	PUBLIC_DRAFT	[Consumer Loan]	2025-03-01T08:37:38.763000
1	67c2c750924afe7a79ec6f26	CONSUMER_LOAN_STATUS	scd_table	PUBLIC_DRAFT	[Consumer Loan, Client]	2025-03-01T08:37:36.903000
2	67c2c74e924afe7a79ec6f25	PRIOR_APPLICATIONS	event_table	PUBLIC_DRAFT	[Prior Application, Client]	2025-03-01T08:37:34.829000
3	67c2c74c924afe7a79ec6f24	NEW_APPLICATION	dimension_table	PUBLIC_DRAFT	[New Application, Client]	2025-03-01T08:37:32.806000

We can also list entities separately:

In [5]:

            
                Copied!
                
display(catalog.list_entities())
display(catalog.list_entities())

	id	name	serving_names	created_at
0	67c2c76dfd2556fdb5ea9b92	Consumer Loan	[CONSUMER_LOAN_ID]	2025-03-01T08:38:06.210000
1	67c2c76dfd2556fdb5ea9b91	Prior Application	[PRIOR_APPLICATION_ID]	2025-03-01T08:38:05.905000
2	67c2c76dfd2556fdb5ea9b90	Client	[CLIENT_ID]	2025-03-01T08:38:05.572000
3	67c2c76cfd2556fdb5ea9b8f	New Application	[NEW_APPLICATION_ID]	2025-03-01T08:38:05.280000

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

In [6]:

            
                Copied!
                
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	67c2c7714e08d83e213816d6	child_parent	Consumer Loan	Client	CONSUMER_LOAN_STATUS	scd_table	True	2025-03-01T08:38:09.286000	None
1	67c2c7704e08d83e213816cd	child_parent	Prior Application	Client	PRIOR_APPLICATIONS	event_table	True	2025-03-01T08:38:08.356000	None
2	67c2c76f3df413286793fb16	child_parent	New Application	Client	NEW_APPLICATION	dimension_table	True	2025-03-01T08:38:07.444000	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