semolina 0.4.0

A Pythonic ORM for querying data warehouse semantic views (Snowflake, Databricks, DuckDB)

Semolina

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The ORM for your Semantic Layer.

Typed models in Python, supporting IDE autocomplete, and a Django-like fluent query interface for the semantic layer of your data warehouse backend.

pip install semolina
pip install semolina[snowflake]
pip install semolina[databricks]

A model maps to a semantic view in your warehouse.

from semolina import (
    SemanticView,
    Metric,
    Dimension,
)


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

Semolina needs a connection pool to talk to your warehouse. Register one before running any queries:

from semolina import register, pool_from_config

pool, dialect = pool_from_config()  # reads .semolina.toml
register("default", pool, dialect=dialect)

Use Model.query() to start building. Chain .metrics() and .dimensions() to select the fields you want, then call .execute():

cursor = (
    Sales.query()
    .metrics(Sales.revenue)
    .dimensions(Sales.country)
    .execute()
)

.execute() returns a SemolinaCursor. Call .fetchall_rows() to get Row objects that support both attribute and dict-style access:

rows = cursor.fetchall_rows()
for row in rows:
    print(row.country, row.revenue)  # attribute access
    print(row["country"])  # dict-style access

You should see output like:

US 1000
US
CA 2000
CA
US 500
US