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KumoRFM can connect directly to Snowflake data warehouses, enabling predictions on enterprise-scale data without moving it out of Snowflake.

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Installation

The Snowflake backend requires the Snowflake connector: 
pip install kumoai[snowflake]

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Quick Start

From a Snowflake notebook (uses the active session automatically): 
import kumoai.experimental.rfm as rfm

graph = rfm.Graph.from_snowflake(schema="MY_SCHEMA")
With explicit credentials: 
graph = rfm.Graph.from_snowflake(
    connection={
        "account": "my_account",
        "user": "my_user",
        "password": "my_password",
        "warehouse": "my_warehouse",
    },
    database="MY_DATABASE",
    schema="MY_SCHEMA",
)
This will:
  1. Connect to the Snowflake database and schema
  2. Discover all tables in the schema
  3. Infer column metadata (data types, semantic types, primary keys, time columns)
  4. Detect foreign key relationships
  5. Print a summary of the inferred metadata and links

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Specifying Tables

Control which tables to include and customize their configuration: 
graph = rfm.Graph.from_snowflake(
    tables=[
        "USERS",                                           # Include by name
        dict(name="ORDERS", source_name="ORDERS_SNAPSHOT"),# Rename source
        dict(name="ITEMS", schema="OTHER_SCHEMA"),         # Different schema
    ],
    database="DEFAULT_DB",
    schema="DEFAULT_SCHEMA",
)
Table configuration options:
KeyDescriptionRequired
nameThe table name used in PQL queriesYes
source_nameThe actual table name in Snowflake (if different from name)No
databaseOverride the default database for this tableNo
schemaOverride the default schema for this tableNo
primary_keyOverride the auto-detected primary keyNo

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Connection Options

There are several ways to establish a Snowflake connection: 1. Active session (Snowflake notebooks): 
# No connection needed — uses the active Snowpark session
graph = rfm.Graph.from_snowflake(schema="MY_SCHEMA")
2. Credentials dictionary: 
graph = rfm.Graph.from_snowflake(connection={
    "account": "my_account",
    "user": "my_user",
    "password": "my_password",
    "warehouse": "my_warehouse",
})
3. Existing connection: 
import snowflake.connector
conn = snowflake.connector.connect(...)
graph = rfm.Graph.from_snowflake(connection=conn, schema="MY_SCHEMA")

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Database and Schema Defaults

The database and schema parameters set defaults for all tables. If not specified, the current database and schema from the active session are used. Individual tables can override these defaults using the database and schema keys in their configuration dictionary.

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Controlling Metadata Inference

graph = rfm.Graph.from_snowflake(
    schema="MY_SCHEMA",
    infer_metadata=False,  # Skip automatic type inference
    verbose=False,         # Suppress output
)

# Manually configure metadata afterwards:
graph.infer_metadata()
graph.infer_links()

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Manual Edge Specification

graph = rfm.Graph.from_snowflake(
    schema="MY_SCHEMA",
    edges=[
        ("ORDERS", "USER_ID", "USERS"),
        ("ORDERS", "ITEM_ID", "ITEMS"),
    ],
)

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Supported Snowflake Types

KumoRFM maps Snowflake data types as follows:
Snowflake TypeKumoRFM DtypeDefault Stype
NUMBER, DECIMAL, INT, BIGINT, FLOAT, DOUBLEfloat or intnumerical
VARCHAR, STRING, TEXT, CHARstringcategorical / text
BOOLEANboolcategorical
DATE, TIMESTAMP, TIMESTAMP*datetimestamp
ARRAYstringlistmulticategorical
VECTORfloatlistsequence