References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-job-automation-overview

Explanation

Step 1: Create a secret scope
Step 2: Add secrets to the scope
Note: dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>") gets the key that has been stored as a secret in a secret scope.
Step 3: Mount the Azure Blob Storage container
You can mount a Blob Storage container or a folder inside a container through Databricks File System - DBFS. The mount is a pointer to a Blob Storage container, so the data is never synced locally.
Note: To mount a Blob Storage container or a folder inside a container, use the following command:
Python
dbutils.fs.mount(
source = "wasbs://<your-container-name>@<your-storage-account-name>.blob.core.windows.net", mount_point = "/mnt/<mount-name>", extra_configs = {"<conf-key>":dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>")}) where:
dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>") gets the key that has been stored as a secret in a secret scope.
References:
https://docs.databricks.com/spark/latest/data-sources/azure/azure-storage.html

You can configure your storage account to accept requests from secure connections only by setting the Secure transfer required property for the storage account. When you require secure transfer, any requests originating from an insecure connection are rejected. Microsoft recommends that you always require secure transfer for all of your storage accounts.
When secure transfer is required, a call to an Azure Storage REST API operation must be made over HTTPS. Any request made over HTTP is rejected.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/storage-require-secure-transfer

Explanation

Box 1: DATEDIFF
DATEDIFF function returns the count (as a signed integer value) of the specified datepart boundaries crossed between the specified startdate and enddate.
Syntax: DATEDIFF ( datepart , startdate, enddate )
Box 2: LAST
The LAST function can be used to retrieve the last event within a specific condition. In this example, the condition is an event of type Start, partitioning the search by PARTITION BY user and feature. This way, every user and feature is treated independently when searching for the Start event. LIMIT DURATION limits the search back in time to 1 hour between the End and Start events.
Example:
SELECT
[user],
feature,
DATEDIFF(
second,
LAST(Time) OVER (PARTITION BY [user], feature LIMIT DURATION(hour, 1) WHEN Event = 'start'), Time) as duration FROM input TIMESTAMP BY Time WHERE Event = 'end' Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-stream-analytics-query-patterns

Explanation
General symptoms of the job hitting system resource limits include:
If the backlog event metric keeps increasing, it's an indicator that the system resource is constrained (either because of output sink throttling, or high CPU).
Note: Backlogged Input Events: Number of input events that are backlogged. A non-zero value for this metric implies that your job isn't able to keep up with the number of incoming events. If this value is slowly increasing or consistently non-zero, you should scale out your job: adjust Streaming Units.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-scale-jobs
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-monitoring