Explanation
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html

Explanation
The answer is,
The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at
8 AM.
Automatically refresh a dashboard
A dashboard's owner and users with the Can Edit permission can configure a dashboard to auto-matically refresh on a schedule. To automatically refresh a dashboard:
* Click the Schedule button at the top right of the dashboard. The scheduling dialog appears.
* Graphical user interface, text, application, email, Teams Description automatically generated
* 2.In the Refresh every drop-down, select a period.
* 3.In the SQL Warehouse drop-down, optionally select a SQL warehouse to use for all the queries.
If you don't select a warehouse, the queries execute on the last used SQL ware-house.
* 4.Next to Subscribers, optionally enter a list of email addresses to notify when the dashboard is automatically updated.
* Each email address you enter must be associated with a Azure Databricks account or con-figured as an alert destination.
* 5.Click Save. The Schedule button label changes to Scheduled.

Explanation
The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.