MariaDB

intugle integrates with MariaDB, allowing you to read data from your tables and views.

Installation

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

pip install "intugle[mariadb]"

This installs the mariadb (MariaDB Connector/Python) and sqlglot libraries.

Linux Dependencies

On Linux, the MariaDB Connector/Python requires the MariaDB C Connector dependencies to be installed on your system. For example, on Ubuntu/Debian:

sudo apt-get install libmariadb3 libmariadb-dev

On CentOS/RHEL:

sudo yum install mariadb-connector-c-devel

Please refer to the MariaDB Connector/Python documentation for more details.

Configuration

To connect to your MariaDB database, you must provide connection credentials in a profiles.yml file at the root of your project. The adapter looks for a top-level mariadb: key.

Example profiles.yml:

mariadb:
  host: <your_mariadb_host>
  port: 3306 # Default MariaDB port
  user: <your_username>
  password: <your_password>
  database: <your_database_name>

Usage

Reading Data from MariaDB

To include a MariaDB table or view in your SemanticModel, define it in your input dictionary with type: "mariadb" and use the identifier key to specify the object name.

Important

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

from intugle import SemanticModel

datasets = {
    "CUSTOMERS": {
        "identifier": "CUSTOMERS", # Must match the key above
        "type": "mariadb"
    },
    "ORDERS_VIEW": {
        "identifier": "ORDERS_VIEW", # Can be a view
        "type": "mariadb"
    }
}

# 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 MariaDB connection, the resulting data product can be materialized as a new table or view directly within your target database.

from intugle import DataProduct

etl_model = {
    "name": "top_customers",
    "fields": [
        {"id": "CUSTOMERS.customer_id", "name": "customer_id"},
        {"id": "CUSTOMERS.name", "name": "customer_name"},
    ]
}

dp = DataProduct()

# Materialize as a view (default)
dp.build(etl_model, materialize="view")

# Materialize as a table
dp.build(etl_model, materialize="table")