How to connect to Snowflake

Install the Snowflake extra

pip install semolina[snowflake]
# or
uv add "semolina[snowflake]"

The Snowflake extra installs adbc-poolhouse[snowflake], which provides the ADBC Snowflake driver and connection pooling.

Configure with .semolina.toml (recommended)

Create a .semolina.toml file in your project root:

# .semolina.toml
[connections.default]
type = "snowflake"
account = "xy12345.us-east-1"
user = "myuser"
password = "mypassword"
database = "analytics"
warehouse = "compute_wh"
# role = ""
# schema = ""

Field	Type	Required	Description
type	str	Yes	Must be "snowflake"
account	str	Yes	Account identifier with region (e.g. xy12345.us-east-1)
user	str	Yes	Snowflake username
password	str	Yes	Snowflake password
database	str	No	Default database
warehouse	str	No	Compute warehouse name
role	str	No	Role to activate for the session
schema	str	No	Default schema

Note

database and warehouse are optional for the query pool: a fully-qualified view name supplies the database, and the warehouse can fall back to your Snowflake user’s default. semolina codegen is stricter and requires both – see How to configure codegen credentials.

Connection pooling is tuned with the shared pool_size, max_overflow, timeout, and recycle fields, documented under Common fields.

Then build and register an engine:

from semolina import register, create_engine

register(
    "default", create_engine("default")
)  # reads [connections.default]

Tip

Use create_engine("analytics") to load a named connection section other than default.

Configure manually

When credentials come from a vault or secrets manager, pass a config object to create_engine():

from adbc_poolhouse import SnowflakeConfig

from semolina import register, create_engine

engine = create_engine(
    SnowflakeConfig(
        account="xy12345.us-east-1",
        user="myuser",
        password="mypassword",
        database="analytics",
        warehouse="compute_wh",
    )
)
register("default", engine)

Run a query

Once an engine is registered, the query API works the same as any backend:

from semolina import SemanticView, Metric, Dimension


class Sales(SemanticView, view="sales"):
    revenue = Metric()
    country = Dimension()


cursor = (
    Sales.query()
    .metrics(Sales.revenue)
    .dimensions(Sales.country)
    .execute()
)
for row in cursor.fetchall_rows():
    print(row.country, row.revenue)

Generated SQL

Snowflake SQL uses AGG() for metrics and double-quoted identifiers:

SELECT AGG("revenue"), "country"
FROM "sales"
GROUP BY ALL

See also

• How to choose and configure a backend – compare connection patterns

• How to connect to Databricks – connect to Databricks metric views

• How to test query code without a warehouse – test queries with a local DuckDB backend