Explanation
There are a number of other resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:
* Not enough processing resources in Stream Analytics to handle the volume of input events.
* Not enough throughput within the input event brokers, so they are throttled.
* Output sinks are not provisioned with enough capacity, so they are throttled. The possible solutions vary widely based on the flavor of output service being used.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-time-handling

Explanation

Box 1: Yes
You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data.
The outcome is a stream that has the same partition scheme. Please see below for an example:
WITH step1 AS (SELECT * FROM [input1] PARTITION BY DeviceID INTO 10),
step2 AS (SELECT * FROM [input2] PARTITION BY DeviceID INTO 10)
SELECT * INTO [output] FROM step1 PARTITION BY DeviceID UNION step2 PARTITION BY DeviceID Note: The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number of partitions for a stream when performing reshuffling using a PARTITION BY statement.
Box 2: Yes
When joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.
Box 3: Yes
Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job.
In general, the best practice is to start with 6 SUs for queries that don't use PARTITION BY.
Here there are 10 partitions, so 6x10 = 60 SUs is good.
Note: Remember, Streaming Unit (SU) count, which is the unit of scale for Azure Stream Analytics, must be adjusted so the number of physical resources available to the job can fit the partitioned flow. In general, six SUs is a good number to assign to each partition. In case there are insufficient resources assigned to the job, the system will only apply the repartition if it benefits the job.
Reference:
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption

Explanation

Box 1: Source
For Copy activity, it requires source and sink linked services to define the direction of data flow.
Copying between a cloud data source and a data source in private network: if either source or sink linked service points to a self-hosted IR, the copy activity is executed on that self-hosted Integration Runtime.
Box 2: Self-hosted integration runtime
A self-hosted integration runtime can run copy activities between a cloud data store and a data store in a private network, and it can dispatch transform activities against compute resources in an on-premises network or an Azure virtual network. The installation of a self-hosted integration runtime needs on an on-premises machine or a virtual machine (VM) inside a private network.
References:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

Explanation

Step 1: Configure Blob storage as input; select items with the TIMESTAMP BY clause The default timestamp of Blob storage events in Stream Analytics is the timestamp that the blob was last modified, which is BlobLastModifiedUtcTime. To process the data as a stream using a timestamp in the event payload, you must use the TIMESTAMP BY keyword.
Example:
The following is a TIMESTAMP BY example which uses the EntryTime column as the application time for events:
SELECT TollId, EntryTime AS VehicleEntryTime, LicensePlate, State, Make, Model, VehicleType, VehicleWeight, Toll, Tag FROM TollTagEntry TIMESTAMP BY EntryTime Step 2: Set up cosmos DB as the output Creating Cosmos DB as an output in Stream Analytics generates a prompt for information as seen below.

Step 3: Create a query statement with the SELECT INTO statement.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-inputs

See the explanation below.
Explanation
1. Launch the Azure portal.
2. Navigate to the settings page of the database db3 that includes the sensitive data you want to mask.
3. Click the Dynamic Data Masking tile that launches the Dynamic Data Masking configuration page.
Note: Alternatively, you can scroll down to the Operations section and click Dynamic Data Masking.

4. In the Dynamic Data Masking configuration page, you may see some database columns that the recommendations engine has flagged for masking.

5. Click ADD MASK for the EmailAddress column
6. Click Save in the data masking rule page to update the set of masking rules in the dynamic data masking policy.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dynamic-data-masking-get-started-portal