**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">


airflow/www/views.py
"""
    Create the list of edges needed to construct the Graph view.
    A special case is made if a TaskGroup is immediately upstream/downstream of another
    TaskGroup or task. Two dummy nodes named upstream_join_id and downstream_join_id are
    created for the TaskGroup. Instead of drawing an edge onto every task in the TaskGroup,
    all edges are directed onto the dummy nodes. This is to cut down the number of edges on
    the graph.
    For example: A DAG with TaskGroups group1 and group2:
        group1: task1, task2, task3
        group2: task4, task5, task6
    group2 is downstream of group1:
        group1 >> group2
    Edges to add (This avoids having to create edges between every task in group1 and group2):
        task1 >> downstream_join_id
        task2 >> downstream_join_id
        task3 >> downstream_join_id
        downstream_join_id >> upstream_join_id
        upstream_join_id >> task4
        upstream_join_id >> task5
        upstream_join_id >> task6
    """
    
    edges_to_add = set()
    
    edges_to_skip = set()
    task_group_map = dag.task_group.get_task_group_dict()
    def collect_edges(task_group):