SQL Server

The SQL Server adapter allows intugle to connect to Microsoft SQL Server and Azure SQL databases. It uses the modern mssql-python driver, which connects directly to SQL Server without needing an external driver manager like ODBC.

OS Dependencies

The mssql-python driver may require additional system-level libraries depending on your operating system (e.g., libltdl on Linux). Before proceeding, please ensure you have installed any necessary prerequisites for your OS.

For detailed, OS-specific installation instructions, please refer to the official Microsoft mssql-python documentation.

Installation

To use this adapter, you must install the necessary dependencies as an extra:

pip install "intugle[sqlserver]"

Profile Configuration

To configure the connection, add a sqlserver entry to your profiles.yml file.

profiles.yml

sqlserver:
  name: my_sqlserver_source # A unique name for this source
  type: sqlserver
  host: "your_server_address"
  port: 1433
  user: "your_username"
  password: "your_password"
  database: "your_database_name"
  schema: "dbo" # Optional, defaults to 'dbo'
  encrypt: true # Optional, defaults to true

Key	Description	Required	Default
name	A unique identifier for this data source connection.	Yes
type	The type of the adapter. Must be sqlserver.	Yes
host	The hostname or IP address of your SQL Server instance.	Yes
port	The port number for the connection.	No	1433
user	The username for authentication.	Yes
password	The password for authentication.	Yes
database	The name of the database to connect to.	Yes
schema	The default schema to use for tables that are not fully qualified.	No	dbo
encrypt	Whether to encrypt the connection. Recommended to keep this true.	No	true

Dataset Configuration

When defining datasets for the SemanticModel, use the type: "sqlserver" and provide the table name as the identifier.

from intugle import SemanticModel

datasets = {
    "customers": {
        "type": "sqlserver",
        "identifier": "Customers" # The name of the table in SQL Server
    },
    "orders": {
        "type": "sqlserver",
        "identifier": "Orders"
    },
    # ... other datasets
}

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