Explanation

Box1: API App

* Events generated from the IoT data sources are sent to the stream ingestion layer through Azure HDInsight Kafka as a stream of messages. HDInsight Kafka stores streams of data in topics for a configurable of time.
* Kafka consumer, Azure Databricks, picks up the message in real time from the Kafka topic, to process the data based on the business logic and can then send to Serving layer for storage.
* Downstream storage services, like Azure Cosmos DB, Azure SQL Data warehouse, or Azure SQL DB, will then be a data source for presentation and action layer.
* Business analysts can use Microsoft Power BI to analyze warehoused data. Other applications can be built upon the serving layer as well. For example, we can expose APIs based on the service layer data for third party uses.
Box 2: Cosmos DB Change Feed
Change feed support in Azure Cosmos DB works by listening to an Azure Cosmos DB container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.
The change feed in Azure Cosmos DB enables you to build efficient and scalable solutions for each of these patterns, as shown in the following image:

References:
https://docs.microsoft.com/bs-cyrl-ba/azure/architecture/example-scenario/data/realtime-analytics-vehicle-iot?vie

Explanation

Box 1: Hash-distributed tables
The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables.
Hash-distributed tables improve query performance on large fact tables.
Box 2: Round-robin distributed tables
A round-robin distributed table distributes table rows evenly across all distributions. The assignment of rows to distributions is random.
Scenario:
ADatum identifies the following requirements for the Health Insights application:
* The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute

Data Discovery & Classification introduces a set of advanced capabilities aimed at protecting data and not just the data warehouse itself. Classification/Labeling - Sensitivity classification labels tagged on the columns can be persisted in the data warehouse itself.
References:
https://azure.microsoft.com/sv-se/blog/announcing-public-preview-of-data-discovery-classification-formicrosoft-azure-sql-data-warehouse/ Overview You are a data engineer for Trey Research. The company is close to completing a joint project with the government to build smart highways infrastructure across North America. This involves the placement of sensors and cameras to measure traffic flow, car speed, and vehicle details.
You have been asked to design a cloud solution that will meet the business and technical requirements of the smart highway.

Explanation
The HDInsight Pig activity in a Data Factory pipeline executes Pig queries on your own or on-demand HDInsight cluster.
References:
https://docs.microsoft.com/en-us/azure/data-factory/transform-data-using-hadoop-pig