Which Azure data storage solution should you recommend for each application?

HOTSPOT

Which Azure data storage solution should you recommend for each application? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Answer:

Explanation:

Health Review: Azure SQL Database

Scenario: ADatum identifies the following requirements for the Health Review application:

– Ensure that sensitive health data is encrypted at rest and in transit.

– Tag all the sensitive health data in Health Review. The data will be used for auditing.

Health Interface: Azure Cosmos DB

ADatum identifies the following requirements for the Health Interface application:

– Upgrade to a data storage solution that will provide flexible schemas and increased throughput for writing data. Data must be regionally located close to each hospital, and reads must display be the most recent committed version of an item.

– Reduce the amount of time it takes to add data from new hospitals to Health Interface.

– Support a more scalable batch processing solution in Azure.

– Reduce the amount of development effort to rewrite existing SQL queries.

Health Insights: Azure SQL Data Warehouse

Azure SQL Data Warehouse is a cloud-based enterprise data warehouse that leverages massively parallel processing (MPP) to quickly run complex queries across petabytes of data. Use SQL Data Warehouse as a key component of a big data solution.

You can access Azure SQL Data Warehouse (SQL DW) from Databricks using the SQL Data Warehouse connector (referred to as the SQL DW connector), a data source implementation for Apache Spark that uses Azure Blob Storage, and PolyBase in SQL DW to transfer large volumes of data efficiently between a Databricks cluster and a SQL DW instance.

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.databricks.com/data/data-sources/azure/sql-data-warehouse.html