Does azure need coding to be useful?
Many people hear about the cloud, and know Microsoft runs a fairly large one known as Microsoft Azure. However most people don’t know how to program, or how to write code for these cloud platforms. So it is ok to wonder if Azure needs coding to be useful.
Microsoft Azure is a cloud environment which provides access to a vast array of services to accomplish many tasks. Many of these services do not require any programming skills from the end user. This includes storage services, machine learning services, search services, migration services and more.
You would likely be quite surprised by what can be accomplished in a cloud environment like Microsoft Azure without needing to write a single line of code. The following will give 10 examples of such services provided by Microsoft Azure.
When first coming to the cloud, you’ll quickly realize you need a place to store files. One service provided by Microsoft Azure which can solve this problem is known as Storage Accounts. Storage accounts allow data to be uploaded and saved into Microsoft Azure in several different ways. There are containers for unstructured storage, there are file shares for typical directory structure layout, there are tables which are used for tabular style storage, and queues for storing lists of work. These are all backed by the Microsoft Azure Storage Accounts.
All of these components of the storage account can be setup and configured via the Azure portal. All files and data can also be uploaded to these components via the portal as well as another tool provided by Microsoft known as the Azure Storage Explorer. This application can be installed on Microsoft Windows, macOS, and Linux machines and I have used the software on each of those. This software gives you a drag and drop interface for uploading data and creating records in the different storage systems.
Other things you can do with the Azure Storage Accounts via the user interface without writing any code is setting settings on the account to control geo replication. Other settings can be configured in the UI for CORS and encryption of the stored files can be enabled as well. Firewall and virtual network access settings can also be configured from the Azure portal. All of this being setup and used without needing to write a single line of code.
Another service which is very similar to Microsoft Azure Storage Accounts is Microsoft Azure Data Lake Storage. The latest version of this data lake service, Gen 2, is actually built on top of the Storage Account service. This service is built for scalable data analytic workloads and normally used when you have large amounts of data that you want to load into the cloud.
With this service you can also use the Azure Portal to load data into the data lake storage location or use the Azure Storage Explorer software as well. The storage for these accounts also appears as containers in the UI which is similar to the unstructured data format container for a regular storage account.
All permissions and file access restrictions can be configured in the Azure portal as well as through the Storage Explorer application. Access Keys for the data lake can be rotated in the user interface without needing to write any code. Lifecycle rules can also be setup and managed from the Azure portal so that files can be automatically set to change the storage pricing tier based on customized rules. These lifecycle rules can also be configured to automatically delete files after a specific time limit from the last modification date, making sure that old files don’t hang around if they aren’t required. All done from the interface settings without writing code.
Something else that can be setup in the Azure portal for the cloud subscription is a Load Balancer. These can be placed in front of a set of virtual machines which are running in either a Virtual Machine Scale Set or Availability Sets in order to distribute traffic among the machines which are usually providing the same service.
To create one of these load balancers without writing code, you simply need to use the Azure portal and click the create resource option. From there search for Load Balancer in the marketplace and select the service by that name provided by Microsoft. In the creation wizard fill in the required details including name, region, type, sku and either select a private IP address, create a new public IP address or select an existing one that is available for use.
Once all the details are entered, you can add any tags you might like to track the usage of this specific load balancer in your billing or simply ignore that section. Then all you need to do is click create and wait for the deployment to finish. Once the deployment has completed, you can load the load balancer and setup the backend pools of machines, setup any health probes you would want to have, apply NAT rules, and change the frontend IP configuration if desired. All of this is accomplished with the Azure Portal and does not require writing a single line of code.
Another service provided by Microsoft Azure is their Content Delivery Network service. This service allows you to have the output of certain services cached at edge nodes around the world so that your customers can have faster responses and load times to items that are loaded regularly for your given service or website.
This service can be created through the Azure portal by selecting it as one of the options from the marketplace when creating a resource in a resource group. You are able to select either Standard Microsoft, Standard Verizon, Standard Akamai or Premium Verizon as the CDN provider depending on your needs or pricing requirements.
Once the CDN has been deployed into a resource group you can add endpoints via the Azure portal user interface. Which for the Standard Microsoft version, you’ll end up with an endpoint domain something like name.azureedge.net. The Origin types that you can select for this are Storage, to place the CDN in front of an Azure Storage Account, storage static website, Cloud Service, Web App, and Custom Origin. Each of these will have different configuration options that are specific to them, but all will end up acting as a cache in front of the service it is being linked to.
As you can see, all of this can be setup and configured right in the Azure Portal and doesn’t require the cloud user to know how to write code to make it happen, which is nice since the end result is a better experience for your customers as their page load times will likely become faster.
Another service that is made available to Microsoft Azure users is their Azure Active Directory service. This service is used to setup user accounts as well as permissions related to those users inside of Microsoft Azure. All of the settings related to the user directory can be setup and configured in the Azure Portal.
With the Azure Active Directory you are also able to create application objects which can be used to allow certain services to sign in via the active directory using standard OAuth technologies. Fine grained user permissions can also be controlled within the Azure Active Directory for all of the different Azure Services available. This way, the users that are setup in the directory will only have access to, and permissions on services and objects that they are permitted to use.
Within the directory, to make permission handling more simple, groups can be setup and users added to those groups. Then these groups can be given certain permissions to the different services that they are allowed to work with. That way users can simply be added or removed from a group when needed instead of directly setting the permissions on the user object in the directory. Again, no code required for any of this.
Related to the Azure Active Directory, but more alongside Azure is the Microsoft Office 365 service. Users in the Azure Active Directory can be given access to the Office 365 service which gives them access to the many different office applications including Microsoft Excel, Microsoft Word, Microsoft Powerpoint, Microsoft Teams, Microsoft OneNote, Microsoft Outlook, Microsoft OneDrive, Dynamics 365 and a whole marketplace of other apps and products.
This suite of tools is very good for productivity on any team and none of them really require any programming knowledge to use. Power BI is another tool available within this suite which allows creating visual dashboards on top of data that you have access to and is done entirely through a visual user interface, or with the Power BI application. For a non coder, this can be a great tool to have access to.
Power Apps and Power Automate are a few other services in this suite which would work in tandem with Microsoft Power BI. The goal of all of them together is to make it very simple for less technical people to build internal web or mobile applications with the data that is already available to the organization. Tasks is another application available within Office 365 which can be used to keep track of remaing work, or personal, items that have yet to be completed.
One service that is provided by Azure, that most non coders would think is out of reach by them would be Microsoft Azure Cognitive Services. This is a suite of machine learning models that Microsoft provides to their cloud customers which enable them to run certain tasks through some very sophisticated processes in order to get insights from their data or customer queries.
This service includes many different models covering many different use cases. The categories mentioned by the cognitive services are Decision, Learning, Speech, Vision, and Web Search. These would include models like Language Understanding, Text Analytics, Translator service, Computer Vision models, Face detection models, Video Indexer, Text to Speech, Speech to Text, Speech Translation, Content Moderator, and many Bing search tools and models. Most of these have interfaces within the Azure portal to use them.
For example the Text Analytics service provides an API Console which once given your API key allows you to run queries from the user interface to the analytics service to get insights back about the supplied text. This will detect things like language, identify entities within the documents, identify key phrases in the documents and give sentiment scores for each entry provided. This can all be accessed from the quick start guide which first loads when you open the Text Analytics Service. The other cognitive services will also have similar options available to them.
Part of the cognitive services provided by Microsoft is the Azure Face API. This is another interesting machine learned model the Azure customers can use. Once deployed into a resource group from the Azure Marketplace, a customer is able to use the API Console within the Azure Portal user interface to get insights about different images they might have access to.
From the keys and endpoint tab, you would copy the key that is required for the service and then go to the Quick Start section and from there open the API console. Within the API console select the region in which the Face API Service was deployed and follow the directions to enter the link to an image with faces to be analyzed.
Once the request is submitted through the API console, you’ll see a response near the bottom. This will indicate the values of the bounding boxes surrounding any faces detected within the image. This is really just to show how complex machine learned models built and designed by Microsoft can be used by anyone these days, even those that are unable to write code to do these tasks themselves.
Another service provided by Microsoft Azure which doesn’t require code to be useful to the cloud customer is Azure Data Factory. This service allows for setting up pipelines to move data from many systems to many other systems all with a graphical interface and several button clicks.
When using this service you’ll normally need to setup your data sources which could be things like Azure Storage Accounts, Azure SQL databases, REST API endpoints, or even other services like Salesforce. Once those are properly setup and given proper credentials to access the corresponding services, they can be used as sources or sinks within the Data Factory service.
When using a source, the data will be pulled from that service and that is linked through a pipeline to another service which ends up being the sink. The pipeline is also capable of making transformations to the data as it is being passed from one service to the other. This could be things like only select certain columns from the source data, changing characters from upper case to lower case, converting the type of certain data fields, or simply dropping certain records that don’t meet a given criteria.
This is all possible due the fact that Azure Data Factory has a simple drag and drop user interface and can be used without needing to write a single line of code to accomplish these tasks.
If you need the ability to migrate systems from either your local on premise environment, or from another cloud, Microsoft Azure offers a service to help with this. This service is called Azure Migrate. This service is designed to help you migrate Windows and Linux servers, SQL and other related databases, and other things like applications or data.
This is accomplished with certain tools made available for the different styles of virtual machines or bare metal servers, or database applications that might be installed on those servers. To use this service you may need to install some software on these systems so that Azure can discover them and determine how to best migrate the systems and data into the cloud, but the end goal is to make this as simple and painless for the cloud customer as possible.
This means that the service is able to do most of these migrations without needing the customer to write any custom code to accomplish the given tasks. If you have a lot of services, databases, servers, or virtual machines that you are looking to migrate to the cloud, then this service is likely something worth looking into to accomplish those goals.
As you can see, there are plenty of services and systems available to Azure customers which allow them to accomplish many tasks without needing to write any code whatsoever. As time goes on, there will definitely be many more of these services created, especially in the machine learning space as the models get better and better and simpler to use.