Apache Airflow ODBC Provider

from __future__ import annotations

import pendulum

from airflow.models.dag import DAG
from airflow.providers.common.sql.operators.sql import SQLExecuteQueryOperator

# Ensure you have an Airflow Connection named 'my_odbc_conn'
# type: ODBC
# host: your_odbc_server
# login: your_username
# password: your_password
# extra: {"driver": "{ODBC Driver 18 for SQL Server}", "autocommit": true}

with DAG(
    dag_id="odbc_example_dag",
    start_date=pendulum.datetime(2023, 1, 1, tz="UTC"),
    schedule=None,
    catchup=False,
    tags=["odbc", "example"],
) as dag:
    create_table = SQLExecuteQueryOperator(
        task_id="create_table",
        sql="""
            CREATE TABLE IF NOT EXISTS my_odbc_table (
                id INT IDENTITY(1,1) PRIMARY KEY,
                value VARCHAR(255)
            );
        """,
        conn_id="my_odbc_conn",
    )

    insert_data = SQLExecuteQueryOperator(
        task_id="insert_data",
        sql="""
            INSERT INTO my_odbc_table (value) VALUES ('test_value_{{ ds }}');
        """,
        conn_id="my_odbc_conn",
    )

    # Note: For fetching data, you'd typically use OdbcHook in a PythonOperator
    # or a custom operator, as SQLExecuteQueryOperator is for execution.

    create_table >> insert_data