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.

:::warning 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.

:::caution 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")