CMIT 336 Week 3 Discussion Select one of the following discussions:  Discussion 1:  Your supervisor has asked you to configure a new system using existing configurations

Discussion 1: 

Your supervisor has asked you to configure a new system using existing configurations. He said to use either an ARM template or a blueprint. What would you suggest and why? When do you think it is appropriate to use an ARM template and when is it not?

I think both would be a good suggestion but, I would suggest using a blueprint when configuring a new system using existing configurations. The reason I would suggest Azure blueprints is because the blueprint service is there to be able to assist with configuration. A blueprint will normally consist of resources groups,policies, role assignments as well as an ARM template. By using Azure blueprints, the relationship that is configured between the definition and the assignment will be preserved so that it can be used again in the future.

I believe that it is best to use an ARM template when the user plans on using syntax which will let the user state the intentions of a deployment are without the need to write programming commands. With an ARM template, the user can specifically pick what resources to use and to deploy. I believe that is it not best to use an ARM template when it comes to implementing infrastructure as a code.

References:

Elliot. E (August 2017). Azure Resource Manager

https://www.red-gate.com/simple-talk/cloud/infrastructure-as-a-service/azure-resource-manager-arm-templates/

Microsoft. (May 2021). What is Azure Blueprints?

https://docs.microsoft.com/en-us/azure/governance/blueprints/overview

Discussion 2:

The deployment of a database and analytics platform that can collect field data from 200 sensors is what Azure is all about. Azure has several services that can complete this task such as Azure SQL Database and Azure Synapse Analytics.

Azure SQL Database is a fully managed database that will take care of most of the back-end functions such as upgrades, patches, and monitoring. Information regarding the sensors can be formatted in a structured column based schema that will be directly imported into the SQL server. Azure SQL has built in monitoring and alerting capabilities that will allow for real time insights regarding the sensors, field data, or other data. The database can also scale up and down based off resource utilization which can be helpful to reuse sensors in other farms and minimize server costs.

Azure Synapse is a big data analytics platform that combines multiple components such as Apache Spark, SQL server, pipelines and studio into one behemoth of a service. Synapse will be able to process field data from different data sources and different formats of data (structured, unstructured) all in one centralized location. Synapse has a feature called pipelines, part of the synapse studio bundle, that help visually see how the data is being transformed from raw data, all the way to the importing into the specific database. Data analysis and visualization can be done using a SQL, Apache Spark, and even using AI and machine learning models.

References:
https://docs.microsoft.com/en-us/azure/azure-sql/database/sql-database-paas-overview

https://docs.microsoft.com/en-us/azure/synapse-analytics/overview-what-is

https://docs.microsoft.com/en-us/azure/synapse-analytics/machine-learning/what-is-machine-learning