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

Topic 4, ADatum Corporation

Case study

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Overview

General Overview

ADatum Corporation is a medical company that has 5,000 physicians located in more than 300 hospitals across the US. The company has a medical department, a sales department, a marketing department, a medical research department, and a human resources department.

You are redesigning the application environment of ADatum.

Physical Locations

ADatum has three main offices in New York, Dallas, and Los Angeles. The offices connect to each other by using a WAN link. Each office connects directly to the Internet. The Los Angeles office also has a datacenter that hosts all the company’s applications.

Existing Environment

Health Review

ADatum has a critical OLTP web application named Health Review that physicians use to track billing, patient care, and overall physician best practices.

Health Interface

ADatum has a critical application named Health Interface that receives hospital messages related to patient care and status updates. The messages are sent in batches by each hospital’s enterprise relationship management (ERM) system by using a VPN. The data sent from each hospital can have varying columns and formats.

Currently, a custom C# application is used to send the data to Health Interface. The application uses deprecated libraries and a new solution must be designed for this functionality.

Health Insights

ADatum has a web-based reporting system named Health Insights that shows hospital and patient insights to physicians and business users. The data is created from the data in Health Review and Health Interface, as well as manual entries.

Database Platform

Currently, the databases for all three applications are hosted on an out-of-date VMware cluster that has a single instance of Microsoft SQL Server 2012.

Problem Statements

ADatum identifies the following issues in its current environment:

– Over time, the data received by Health Interface from the hospitals has slowed, and the number of messages has increased.

– When a new hospital joins ADatum, Health Interface requires a schema modification due to the lack of data standardization.

– The speed of batch data processing is inconsistent.

Business Requirements

Business Goals

ADatum identifies the following business goals:

– Migrate the applications to Azure whenever possible.

– Minimize the development effort required to perform data movement.

– Provide continuous integration and deployment for development, test, and production environments.

– Provide faster access to the applications and the data and provide more consistent application performance.

– Minimize the number of services required to perform data processing, development, scheduling, monitoring, and the operationalizing of pipelines.

Health Review Requirements

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 Requirements

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 Requirements

ADatum identifies the following requirements for the Health Insights application:

– The analysis of events must be performed over time by using an organizational date dimension table.

– The data from Health Interface and Health Review must be available in Health Insights within 15 minutes of being committed.

– 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.

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.

View Answer

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

Reference: https://docs.databricks.com/data/data-sources/azure/sql-data-warehouse.html