**Description**

`BigQueryInsertJobOperator` requires the submission of a `configuration` parameter in the form of a dict.  Unfortunately, if this contains a large SQL query - especially one that is formatted with new lines - then this cannot currently be rendered very nicely in the UI.

<img width="1670" alt="Screenshot 2021-07-15 at 15 39 33" src="https://user-images.githubusercontent.com/967119/125806943-839d57a9-d4a0-492d-b130-06432b095239.png">

**Use case / motivation**

The issue with this is that it's impossible to copy and paste the rendered query out of the Airflow UI, into a BigQuery browser and run it without lots of manual edits which is time wasted when troubleshooting problems.

**Are you willing to submit a PR?**

Yes.  My current thought process around this would be to add an optional SQL parameter to the operator which, if provided, would be added into the configuration and could therefore have its own template field and SQL renderer.

e.g.

<img width="1570" alt="Screenshot 2021-07-14 at 14 18 09" src="https://user-images.githubusercontent.com/967119/125808200-5f30b8f4-4def-48a7-8223-82afdc65c973.png">


tests/www/views/test_views.py
from airflow.www import app
    return app.create_app(testing=True)
def test_mark_task_instance_state(test_app):
    """
    Test that _mark_task_instance_state() does all three things:
    - Marks the given TaskInstance as SUCCESS;
    - Clears downstream TaskInstances in FAILED/UPSTREAM_FAILED state;
    - Set DagRun to QUEUED.
    """
    from airflow.models import DAG, DagBag, TaskInstance
    from airflow.operators.dummy import DummyOperator
    from airflow.utils.session import create_session
    from airflow.utils.state import State
    from airflow.utils.timezone import datetime
    from airflow.utils.types import DagRunType
    from airflow.www.views import Airflow
    start_date = datetime(2020, 1, 1)
    with DAG("test_mark_task_instance_state", start_date=start_date) as dag:
        task_1 = DummyOperator(task_id="task_1")
        task_2 = DummyOperator(task_id="task_2")
        task_3 = DummyOperator(task_id="task_3")
        task_4 = DummyOperator(task_id="task_4")
        task_5 = DummyOperator(task_id="task_5")
        task_1 >> [task_2, task_3, task_4, task_5]
    dagrun = dag.create_dagrun(
        start_date=start_date, execution_date=start_date, state=State.FAILED, run_type=DagRunType.SCHEDULED
    )
    def get_task_instance(session, task):