Introduction To Azure Architecture - Part Two

In my previous article, Introduction To Azure Architecture - Part One, I covered some of the elements of Azure architecture. I will cover rest of the architecture in this article. So, let's see them one by one.
in Analytics, we have the following things.

  • HD Insight
    HD Insight is a cloud service from Microsoft that makes it easy and fast to process a large amount of data. It includes Apache, Hadoop and other technologies for big data analysis.

  • Machine Learning
    Using machine learning, we can deploy predictive analytics models in the cloud. 

  • Cognitive Services
    It includes different APIs, such as face recognition API, speech recognition API etc. and using these APIs, we can integrate them within our own applications and that will help us to make our applications more interactive and intelligent of course.

  • Stream Analytics
    Using stream analytics, we will be able to analyze the stream in real-time  and take the necessary actions.

  • Data Lake Analytics
    Using Data Lake analytics, we will be able to analyze the data on a hyper-scale. Data lake analytics and data lake store are related to each other. Basically, Data Lake Store is used to store a large amount of data and data lake analytics is used to analyze the data on a massive scale. 
Enterprise Application Integration(EAI)
It includes the following things.
  • Service Bus
    Service Bus includes queues, topics, and subscription. Service Bus is used to interact with the different services on Azure. We can consider this as a bus which takes the data and delivers it to other services. It is basically used for communication purposes. 

  • Biztalk Service
    Biztalk services are a flagship product of Microsoft in integration and they're offering the same in the cloud.

  • Logic Apps
    Using logic apps, we will be able to build workflows which means that logic apps are used to build processes and schedule them according to our convenience and we can even automate them based on our needs as workflows.
Internet of Things(IoT)
IoT has the  following things.
  • IoT Hub
    Here, we can register a device and get the data from the device and store it for later processing so that we can be ready to take action when an IoT device needs our attention.

  • Event Hubs
    We can say that it is a subset of IoT hub which is used to store events with a very high throughput. So, if we have hundreds of sensors sending events, then we can use the event hub to store and process the data.

  • Notification Hubs
    Using Notification Hub, we will be able to send notifications to  different devices like Windows, iOS, and Android etc.
In Security, we have the following things.
  • Azure AD (Active Directory)
    Azure Active Directory is Microsoft Multi-tenant cloud-based directory and identity management service through which we will sync our on-premises active directory with Azure Active Directory. This is the more general approach that many people are taking nowadays.

  • Key vault
    Using key vault, we will be able to streamline key management processes and securely store our keys. Most of the Azure services have access keys associated with them and it is always good to manage those keys in the key vault for security purposes.

  • Security Center
    Using security center, we will be able to set policies and see the recommendations and if there is any threat identified we can manage it in the Security Center.

  • Azure B2C
    Azure B2C is basically a cloud identity management service for Web Apps and Mobile Applications. So if we have many users using these applications and if we want to manage their identity then Azure B2C is the perfect choice I would say.

  • Azure Multi-Factor Authentication(MFA)
    Azure multi-factor authentication is a two-factor authentication service from Microsoft. It is the most secure and most used technique for authentication nowadays.
In Monitoring, we have the following things.
  • Azure Monitor
    The Azure Monitor is a basic monitoring tool where we can monitor all the metrics, activity, diagnostic logs for all Azure Services at the same place.

  • Log Analytics
    Using Log Analytics, we will be able to get data or logs from different sources and whether it is an on-premise or Azure cloud, we can pull the data from all these data sources and apply analytics on top of it.

  • Advisor
    Azure Advisor is a very useful feature of Azure which advises us how we can minimize the cost of Azure and application insight which is quite similar to log analytics except it focuses more on the application rather than infrastructure or server side.

  • Automation
    Using Automation, we will be able to automate the different Azure tasks, which is so interesting. For example, if we want to shut down a virtual machine, we can do it easily using Azure Automation and so on.
Microsoft provides different tools using which we will be able to manage our Azure Services easily. Here is a list:
  • Visual Studio
  • PowerShell
  • CLI (command line interface)
  • ARM Templates 
  • Storage Explorer and Emulator.
We will see each and every tool thoroughly in our upcoming articles.