Topic 2, Litware, inc.
Case study
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Overview
Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared foods, such as sandwiches and pizzas.
Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout.
Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks notebooks.
Requirements
Business Goals
Litware wants to create a new analytics environment in Azure to meet the following requirements:
✑ See inventory levels across the stores. Data must be updated as close to real time as possible.
✑ Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products.
✑ Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data.
Technical Requirements
Litware identifies the following technical requirements:
✑ Minimize the number of different Azure services needed to achieve the business goals.
✑ Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware.
✑ Ensure that the analytical data store is accessible only to the company’s on-premises network and Azure services.
✑ Use Azure Active Directory (Azure AD) authentication whenever possible.
✑ Use the principle of least privilege when designing security.
✑ Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed.
✑ Limit the business analysts’ access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
✑ Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion.
Planned Environment
Litware plans to implement the following environment:
✑ The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
✑ Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
✑ Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
✑ Daily inventory data comes from a Microsoft SQL server located on a private network.
✑ Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next year.
✑ Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every four hours.
✑ Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure.
What should you do to improve high availability of the real-time data processing solution?
A . Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
B . Deploy a High Concurrency Databricks cluster.
C . Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.
D . Set Data Lake Storage to use geo-redundant storage (GRS).
Answer: A
Explanation:
Guarantee Stream Analytics job reliability during service updates
Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure’s paired region model.
Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure
Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-job-reliability
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