Apache Airflow Salesforce Provider

import os
from datetime import datetime
from airflow.models.dag import DAG
from airflow.providers.salesforce.hooks.salesforce import SalesforceHook
from airflow.providers.salesforce.operators.salesforce import SalesforceToTableOperator

with DAG(
    dag_id='salesforce_example_dag',
    start_date=datetime(2023, 1, 1),
    schedule_interval=None,
    catchup=False,
    tags=['salesforce', 'example'],
) as dag:
    # Example of using SalesforceToTableOperator to extract data
    extract_contacts = SalesforceToTableOperator(
        task_id='extract_salesforce_contacts',
        salesforce_conn_id='salesforce_default', # Ensure this connection is configured in Airflow UI
        sobjects=['Contact'],
        table_name='airflow_contacts',
        selected_fields=['Id', 'FirstName', 'LastName', 'Email'],
        where_clause="MailingState = 'CA'",
        # Optional: You might push to a database table or another destination
        # E.g., postgres_conn_id='postgres_default'
        # For this example, we'll just demonstrate extraction.
    )

    # Example of using SalesforceHook directly (e.g., in a PythonOperator)
    def get_salesforce_data(**kwargs):
        hook = SalesforceHook(salesforce_conn_id='salesforce_default')
        sf_client = hook.get_conn()
        # Query the Salesforce API directly
        records = sf_client.query("SELECT Id, Name FROM Account LIMIT 5")
        print(f"Fetched {len(records['records'])} Account records.")
        return records['records']

    # You'd typically use a PythonOperator to wrap the hook usage
    # from airflow.operators.python import PythonOperator
    # fetch_data_task = PythonOperator(
    #     task_id='fetch_data_with_hook',
    #     python_callable=get_salesforce_data,
    # )
    # extract_contacts >> fetch_data_task