Snowflake

intugle integrates with Snowflake, allowing you to read data from Snowflake tables and deploy your SemanticModel as a Semantic View in your Snowflake account.

Installation

To use intugle with Snowflake, you must install the optional dependencies:

pip install "intugle[snowflake]"

This installs the snowflake-snowpark-python library.

Configuration

The Snowflake adapter can connect using credentials from a profiles.yml file or automatically use an active session when running inside a Snowflake Notebook.

Connecting from an External Environment

When running intugle outside of a Snowflake Notebook, you must provide connection credentials in a profiles.yml file at the root of your project. The adapter looks for a top-level snowflake: key.

Example profiles.yml:

snowflake:
  type: snowflake
  account: <your_snowflake_account>
  user: <your_username>
  password: <your_password>
  role: <your_role>
  warehouse: <your_warehouse>
  database: <your_database>
  schema: <your_schema>

Connecting from a Snowflake Notebook

When your code is executed within a Snowflake Notebook, the adapter automatically detects and uses the notebook's active Snowpark session. No configuration is required.

Usage

Reading Data from Snowflake

To include a Snowflake table in your SemanticModel, define it in your input dictionary with type: "snowflake" and use the identifier key to specify the table name.

Important

The dictionary key for your dataset (e.g., "CUSTOMERS") must exactly match the table name specified in the identifier.

from intugle import SemanticModel

datasets = {
    "CUSTOMERS": {
        "identifier": "CUSTOMERS", # Must match the key above
        "type": "snowflake"
    },
    "ORDERS": {
        "identifier": "ORDERS", # Must match the key above
        "type": "snowflake"
    }
}

# Initialize the semantic model
sm = SemanticModel(datasets, domain="E-commerce")

# Build the model as usual
sm.build()

Materializing Data Products

When you use the DataProduct class with a Snowflake connection, the resulting data product will be materialized as a new table directly within your Snowflake schema.

Deploying the Semantic Model

Once your semantic model is built, you can deploy it to Snowflake using the deploy() method. This process performs two actions:

1. Syncs Metadata: It updates the comments on your physical Snowflake tables and columns with the business glossaries from your intugle model.
2. Creates Semantic View: It constructs and executes a CREATE OR REPLACE SEMANTIC VIEW statement in your target database and schema.

# Deploy the model to Snowflake
sm.deploy(target="snowflake")

# You can also provide a custom name for the view
sm.deploy(target="snowflake", model_name="my_custom_semantic_view")

Required Permissions

To successfully deploy a semantic model, the Snowflake role you are using must have the following privileges:

• USAGE on the target database and schema.
• CREATE SEMANTIC VIEW on the target schema.
• ALTER TABLE permissions on the source tables to update their comments.

Next Steps: Chat with your Data using Cortex Analyst

Now that you have deployed a Semantic View, you can use Snowflake Cortex Analyst to interact with your data using natural language. Cortex Analyst leverages the relationships and context defined in your Semantic View to answer questions without requiring you to write SQL.

To get started, navigate to AI & ML -> Cortex Analyst in the Snowflake UI and select your newly created view.