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

Explanation
Explanation:
Azure Stream Analytics supports user-defined aggregates (UDA) written in JavaScript, it enables you to implement complex stateful business logic. Within UDA you have full control of the state data structure, state accumulation, state decumulation, and aggregate result computation.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-javascript-user-defined-aggregates

Explanation
Dynamic Data Masking is designed to simplify application development by limiting data exposure in a set of pre-defined queries used by the application. While Dynamic Data Masking can also be useful to prevent accidental exposure of sensitive data when accessing a production database directly, it is important to note that unprivileged users with ad-hoc query permissions can apply techniques to gain access to the actual data. If there is a need to grant such ad-hoc access, Auditing should be used to monitor all database activity and mitigate this scenario.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/security/dynamic-data-masking

Explanation

Box 1: FileCapacity
FileCapacity is the amount of storage used by the storage account's File service in bytes.
Box 2: Avg
The aggregation type of the FileCapacity metric is Avg.
Scenario:
All services and processes must be resilient to a regional Azure outage.
All Azure services must be monitored by using Azure Monitor. On-premises SQL Server performance must be monitored.
References:
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/metrics-supported
Topic 3, Litware, inc
Overview
General Overview
Litware, Inc, is an international car racing and manufacturing company that has 1,000 employees. Most employees are located in Europe. The company supports racing teams that complete in a worldwide racing series.
Physical Locations
Litware has two main locations: a main office in London, England, and a manufacturing plant in Berlin, Germany.
During each race weekend, 100 engineers set up a remote portable office by using a VPN to connect the datacentre in the London office. The portable office is set up and torn down in approximately 20 different countries each year.
Existing environment
Race Central
During race weekends, Litware uses a primary application named Race Central. Each car has several sensors that send real-time telemetry data to the London datacentre. The data is used for real-time tracking of the cars.
Race Central also sends batch updates to an application named Mechanical Workflow by using Microsoft SQL Server Integration Services (SSIS).
The telemetry data is sent to a MongoDB database. A custom application then moves the data to databases in SQL Server 2017. The telemetry data in MongoDB has more than 500 attributes. The application changes the attribute names when the data is moved to SQL Server 2017.
The database structure contains both OLAP and OLTP databases.
Mechanical Workflow
Mechanical Workflow is used to track changes and improvements made to the cars during their lifetime.
Currently, Mechanical Workflow runs on SQL Server 2017 as an OLAP system.
Mechanical Workflow has a named Table1 that is 1 TB. Large aggregations are performed on a single column of Table 1.
Requirements
Planned Changes
Litware is the process of rearchitecting its data estate to be hosted in Azure. The company plans to decommission the London datacentre and move all its applications to an Azure datacentre.
Technical Requirements
Litware identifies the following technical requirements:
* Data collection for Race Central must be moved to Azure Cosmos DB and Azure SQL Database. The data must be written to the Azure datacentre closest to each race and must converge in the least amount of time.
* The query performance of Race Central must be stable, and the administrative time it takes to perform optimizations must be minimized.
* The datacentre for Mechanical Workflow must be moved to Azure SQL data Warehouse.
* Transparent data encryption (IDE) must be enabled on all data stores, whenever possible.
* An Azure Data Factory pipeline must be used to move data from Cosmos DB to SQL Database for Race Central. If the data load takes longer than 20 minutes, configuration changes must be made to Data Factory.
* The telemetry data must migrate toward a solution that is native to Azure.
* The telemetry data must be monitored for performance issues. You must adjust the Cosmos DB Request Units per second (RU/s) to maintain a performance SLA while minimizing the cost of the Ru/s.
Data Masking Requirements
During rare weekends, visitors will be able to enter the remote portable offices. Litware is concerned that some proprietary information might be exposed. The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
* Only show the last four digits of the values in a column named SuspensionSprings.
* Only Show a zero value for the values in a column named ShockOilWeight.

Explanation
Scale read and write throughput globally. You can enable every region to be writable and elastically scale reads and writes all around the world. The throughput that your application configures on an Azure Cosmos database or a container is guaranteed to be delivered across all regions associated with your Azure Cosmos account. The provisioned throughput is guaranteed up by financially backed SLAs.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/distribute-data-globally