3. Register Entities

Registering entities represented in grocery dataset¶

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

Taking our grocery scenario as an example, we can view "Customer", "Invoice", and "Item" 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]:

import featurebyte as fb

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

catalog_name = "Grocery Dataset Tutorial"
catalog = fb.Catalog.activate(catalog_name)

import featurebyte as fb # Set your profile to the tutorial environment fb.use_profile("tutorial") catalog_name = "Grocery Dataset Tutorial" catalog = fb.Catalog.activate(catalog_name)

16:05:47 | WARNING  | Service endpoint is inaccessible: http://featurebyte-server:8088
16:05:47 | INFO     | Using profile: tutorial
16:05:47 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
16:05:47 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
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

As previously discussed, we'll establish the following entities:

• customer - an individual shopping at the stores
• invoice - a record of the customer's purchase
• item - individual items on the invoice
• product - products available in the store for purchase
• productgroup - the category or group a product falls under
• frenchstate - a region in France (since our dataset revolves around French grocery stores and their customers)

It's worth noting that the count of entities doesn't necessarily have to align with the number of tables. An entity is a business-oriented term, and multiple entities might be represented within a single table.

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]:

catalog.create_entity(name="customer", serving_names=["GROCERYCUSTOMERGUID"])
catalog.create_entity(name="invoice", serving_names=["GROCERYINVOICEGUID"])
catalog.create_entity(name="item", serving_names=["GROCERYINVOICEITEMGUID"])
catalog.create_entity(name="product", serving_names=["GROCERYPRODUCTGUID"])
catalog.create_entity(name="productgroup", serving_names=["PRODUCTGROUP"])
catalog.create_entity(name="frenchstate", serving_names=["FRENCHSTATE"])

catalog.create_entity(name="customer", serving_names=["GROCERYCUSTOMERGUID"]) catalog.create_entity(name="invoice", serving_names=["GROCERYINVOICEGUID"]) catalog.create_entity(name="item", serving_names=["GROCERYINVOICEITEMGUID"]) catalog.create_entity(name="product", serving_names=["GROCERYPRODUCTGUID"]) catalog.create_entity(name="productgroup", serving_names=["PRODUCTGROUP"]) catalog.create_entity(name="frenchstate", serving_names=["FRENCHSTATE"])

Out[2]:

Entity

name	frenchstate
created_at	2024-06-12 08:05:48
updated_at	None
description	None
serving_names	['FRENCHSTATE']
catalog_name	Grocery Dataset Tutorial

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]:

customer_table = catalog.get_table("GROCERYCUSTOMER")
invoice_table = catalog.get_table("GROCERYINVOICE")
items_table = catalog.get_table("INVOICEITEMS")
product_table = catalog.get_table("GROCERYPRODUCT")

customer_table = catalog.get_table("GROCERYCUSTOMER") invoice_table = catalog.get_table("GROCERYINVOICE") items_table = catalog.get_table("INVOICEITEMS") product_table = catalog.get_table("GROCERYPRODUCT")

In [4]:

# tag the entities for the grocery customer table
customer_table.GroceryCustomerGuid.as_entity("customer")
customer_table.State.as_entity("frenchstate")

# tag the entities for the grocery invoice table
invoice_table.GroceryInvoiceGuid.as_entity("invoice")
invoice_table.GroceryCustomerGuid.as_entity("customer")

# tag the entities for the grocery items table
items_table.GroceryInvoiceItemGuid.as_entity("item")
items_table.GroceryInvoiceGuid.as_entity("invoice")
items_table.GroceryProductGuid.as_entity("product")

# tag the entities for the grocery product table
product_table.GroceryProductGuid.as_entity("product")
product_table.ProductGroup.as_entity("productgroup")

# tag the entities for the grocery customer table customer_table.GroceryCustomerGuid.as_entity("customer") customer_table.State.as_entity("frenchstate") # tag the entities for the grocery invoice table invoice_table.GroceryInvoiceGuid.as_entity("invoice") invoice_table.GroceryCustomerGuid.as_entity("customer") # tag the entities for the grocery items table items_table.GroceryInvoiceItemGuid.as_entity("item") items_table.GroceryInvoiceGuid.as_entity("invoice") items_table.GroceryProductGuid.as_entity("product") # tag the entities for the grocery product table product_table.GroceryProductGuid.as_entity("product") product_table.ProductGroup.as_entity("productgroup")

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 [5]:

display(catalog.list_tables())

display(catalog.list_tables())

	id	name	type	status	entities	created_at
0	666956c78080c62d0dc616e2	GROCERYPRODUCT	dimension_table	PUBLIC_DRAFT	[product, productgroup]	2024-06-12T08:05:27.992000
1	666956c58080c62d0dc616e1	INVOICEITEMS	item_table	PUBLIC_DRAFT	[item, invoice, product]	2024-06-12T08:05:26.172000
2	666956c38080c62d0dc616e0	GROCERYINVOICE	event_table	PUBLIC_DRAFT	[invoice, customer]	2024-06-12T08:05:24.205000
3	666956c28080c62d0dc616df	GROCERYCUSTOMER	scd_table	PUBLIC_DRAFT	[customer, frenchstate]	2024-06-12T08:05:22.270000

We can also list entities separately:

In [6]:

display(catalog.list_entities())

display(catalog.list_entities())

	id	name	serving_names	created_at
0	666956dc09f6984daf191c96	frenchstate	[FRENCHSTATE]	2024-06-12T08:05:48.452000
1	666956dc09f6984daf191c95	productgroup	[PRODUCTGROUP]	2024-06-12T08:05:48.244000
2	666956db09f6984daf191c94	product	[GROCERYPRODUCTGUID]	2024-06-12T08:05:48.032000
3	666956db09f6984daf191c93	item	[GROCERYINVOICEITEMGUID]	2024-06-12T08:05:47.823000
4	666956db09f6984daf191c92	invoice	[GROCERYINVOICEGUID]	2024-06-12T08:05:47.625000
5	666956db09f6984daf191c91	customer	[GROCERYCUSTOMERGUID]	2024-06-12T08:05:47.417000

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

In [7]:

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	666956dfddd5be620a410f78	child_parent	product	productgroup	GROCERYPRODUCT	dimension_table	True	2024-06-12T08:05:51.721000	None
1	666956df4deaed1e05217b70	child_parent	item	product	INVOICEITEMS	item_table	True	2024-06-12T08:05:51.218000	None
2	666956deec7bc32effdab779	child_parent	item	invoice	INVOICEITEMS	item_table	True	2024-06-12T08:05:51.003000	None
3	666956deec7bc32effdab774	child_parent	invoice	customer	GROCERYINVOICE	event_table	True	2024-06-12T08:05:50.525000	None
4	666956deddd5be620a410f70	child_parent	customer	frenchstate	GROCERYCUSTOMER	scd_table	True	2024-06-12T08:05:50.038000	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

SDK reference for¶

• Entity
• Catalog.create_entity()
• TableColumn.as_entity()