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Azure Study Guide

Why Microsoft Azure Certifications Are Career Changers

Introduction:

In the recent years, the entire concept of computing has seen a drastic change and seen a lot of improvement and evolved from traditional computing to cloud computing. So the important aspect of cloud computing is that the data is shared across the cloud and the data is available on the demand.

With the help of cloud computing facilities, all the computing resources are shared across so that the data can be accessed from any part of the world with a minimal effort. With cloud computing got into the pictures, users and enterprises have the possibility to store the data in the cloud.

It has seen a lot of attraction because it is one of the cost-effective solutions where the companies don’t have to invest in any of the infrastructure cost personally.

If you would like to become a Microsoft Azure certified professional, then visit Mindmajix – A Global online training platform: “Azure online Training”  Course.  This course will help you to achieve excellence in this domain.

The use of Cloud computing has not only provides benefits to the organizations, they provide more exclusive benefits for the individuals.

The following are few unique offering from Azure Cloud services which will help the individuals and the organizations leverage and build custom applications.

1. Paas Capabilities:

Paas stands for Platform as a service.

Within the current market, Azure is one of the most prominently used. Compared to all other competitors Azure has stronger capabilities and has more to offer its customers.  It provides Infrastructure capabilities and also strong coding platform where an individual can build an entire solution.

All the required tools for a developer is readily available for them to build the next generation applications. In continuation, all of the applications can be securely and safely hosted on the cloud platform.

Overall, the Azure cloud provides a unique platform for the individuals to try out all the options and build custom-made solutions. If you are aware of .Net platform which is part of Microsoft, it makes sense to use Azure cloud. As both go hand in hand and has more options for the users to try out.

Related Article: Azure App Services

2. Better Integration Possibility:

As we all know that. Net is one of the most commonly used coding platforms where millions of programmers have their hands on. Providing cloud services to them means a lot. It provides unique opportunities for them where the integrations are not that complex because all are provided by Microsoft itself. So the integration is not a challenge for the users, a lot of user base or fan base is available for support.

With the support in place, it has become one of the favorite services for many of the individuals. Using these services together, the programmers will be able to build next level applications and deploy the Azure cloud services.

All the services are offered by Microsoft, a close integration can be expected and most of the application that is built on this platform can be bulletproof and one cannot expect any loose ends.

This is a benefit for the programmers who are already into.Net coding platform. So it is advisable to concentrate more on the cloud services offered by Microsoft rather than going to another cloud service providers. Where the close integration cannot be possible with the third party vendors. So building up skills on Azure will be a very good suggestion.

3. Devops Implementation:

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Azure cloud services provide “Dev-ops” connections which are widely used in the current software industries. Using this will help the project teams to build secured applications and managing production deployments is made a breeze. The controls provided by the Azure cloud services will help the teams to monitor, manage and continuously fine-tune the entire application.

Most of the activities are automated so there is no point of manual errors, using dev-ops the teams will be able to test the new functionalities simultaneously in different environments and push them to the production environment in the span of time. Unlike the traditional process of moving files from one environment to another environment manually, following Dev-ops process is exceptionally vital for today’s business. This skill is appreciated by many companies out and definitely an area to explore. This will help you fetch good salary package and also a unique skill set that will differentiate from the rest of the individuals in the market.  

A lot of help is available from the community and youtube videos about this. Please spend time updating your skills, especially Dev-ops and how it is beneficial for you in your career.

Related Article: Introduction To Azure DevOps

4. .Net compatibility:

The .Net coding platform has been in existence for quite a long time and most of the businesses have accommodated their software applications which are build based on this platform. So Azure cloud services actually built or brought into existence by taking this aspect into consideration. Microsoft wanted to provide a value ad cloud services for their users where their existing applications and also the modern age application can work together hand in hand with one single cloud, i.e. Microsoft Azure.

This is one of the main advantages for Azure when compared to their competitors like Amazon AWS and rest of the vendors. Executing them .Net based applications or software in the Azure cloud is very simple and it has been a choice for most of the organizations.

Considering this aspect, it is definitely a good choice to learn this skill and also understand the unique services that are offered by Azure services. By updating your skill set it will help you to implement new processes and procedures within the current development phase and also build more secure and safe applications.

Related Article: AWS Vs Azure

5. Security offering:

Azure cloud offers a standardized process which is known as Security Development Lifecycle (SDL). This means that the applications or the services that are hosted on the Azure cloud are 100% secured and well protected.

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Also, Azure platform has some recognition, where they are the very first ones to be approved by European data protection authorities. They have also bagged another certification, i.e. International standard for cloud privacy, ISO 27018.

Looking at these they are definitely best in this space and undoubtedly the leader in cloud offering service provider. So it gives a breather for the organizations to completely focus on the development aspect rather than worrying about safety and security of the hosted platform.

It is a good option for the individuals who are into this space all ready to go through a bit more in detail about the security and increase your awareness about Azure cloud and their services.

6. Able to build Hybrid solutions with super cloud connectivity:

Azure cloud services have lot of advantages compared to the rest of the service providers in the market. Azure services have already provided a wide range of hybrid connections which includes Virtual private networks (VPNs), content delivery networks (CDN’s) and ExpressRoute connections which aid improved usability and also the performance.

Most of the other service providers are still not providing these services completely.

7. Complete Environment:

The main difficulty with most of the software applications is not having a perfect environment for complete testing. Microsoft Azure Cloud has also addressed this as it provides close integrations for the overall solutions. The applications built using their platforms will help the organizations to develop, test and deploy easily.

All the mobile and web applications are completely integrated using their API and the teams can kick-start the development processes.

8. Learning Curve:

The learning curve with Azure is not that much compared to the competitors. The cloud service is built in such a way that it works with both existing and new applications that are already built on the .NET platform. So to learn the new offerings from Azure, it is definitely a worthy option.

A lot of help is available in the online medium in the form of support groups or video tutorials. So the learning curve is not that hard compared to the other competitors.

Tools like Xcode, Hadoop, Github and Visual Studios, Eclipse etc are available with Azure cloud.

9. Enterprise Agreement Advantage:

All the organizations that are already using Microsoft software for their development activities then they are automatically enrolled under the “Enterprise Agreement” advantage. So the use of this agreement will help the organizations to get competitive prices and extra discounts on the new software products and on the Azure cloud services. This is a boon for all the organizations so that they can try out all different services from Microsoft without actually paying a hefty amount. Using this facility small companies can also afford cloud services at minimal prices and offer a great value-added product to the market.

10. Beneficial Cloud Service Provider:

Choosing the right cloud service provider is very important for businesses because all of their data or processing is dependent on their availability. Azure guarantees 99.9% uptime where there are no technical glitches are seen. Also, Azure cloud services provide Paas, hybrid solutions and also an array of beneficial services.

So the uptime of the applications is not at all compromised.

Salary trends for  Azure Cloud service individuals:

As the skill is special compared to the other standard technologies, the individuals who are into cloud computing gets more salary when compared to others. The average salary for the individual who is into Azure is about $53,602 per annum. IF you have made it to the senior level where you act as a Senior Software Architect, then you can expect about $164,170.

Also comparing the industry standard across different companies, the basic average salary per annum is about $40,914.

Certifications that can  boost your career:

The services or the options that are available in the Azure cloud are countless and one cannot explore all the benefits in a single day. To make sure you project your expertise about Azure cloud to the market, certifications from Microsoft is definitely a good way to project your skills.

Few of the certifications are listed below will definitely add value to your profile and make you stand out.

MCSD: Azure Solution Architect
MCSA: Linux on Azure
Microsoft Specialist: Developing Microsoft Azure Solutions
Microsoft Specialist: Implementing Microsoft Azure Infrastructure Solutions
Most of these certificates are not straightforward and some needs to have to quality certain prerequisites.

If you are interested to learn Azure and build a career in Cloud Computing? Then check out our Microsoft Azure Certification Training Course at your near Cities

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These courses are incorporated with Live instructor-led training, Industry Use cases, and hands-on live projects. This training program will make you an expert in Microsoft Azure and help you to achieve your dream job.

Frequently asked Azure Interview Questions & Answers

 

 

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Azure Study Guide

What Is Azure IoT Edge | Overview Of Microsoft Azure IoT Edge

IoT Edge – Extend cloud intelligence to edge devices

With the help of Azure IoT Edge, we can make hybrid cloud and Internet of Things solutions into reality. Actually, Azure IoT Edges contains infrastructure and modules for creating gateway solutions in IoT. An edge device is secure within the IoT context, and cab be manageable asset that is connected and to the cloud. Here are the features:

  • With the edge you can move artificial intelligence from cloud
  • Edge analytics as service
  • Deploy and Rum IoT solutions from cloud to edge
  • Manages devices from cloud centrally
  • Can operate with offline and isolated connectivity
  • We can ensure real-time decisions
  • Can connect new and legacy devices with IoT
  • Lowers the bandwidth costs & cloud management.

Check out the Infographics on Azure IoT Edge:

Infographics on Azure IoT Edge

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Azure Study Guide

Azure Data Lake | Learn Azure Data Lake Architecture

Introduction to Azure Data Lake

Azure Data Lake is a Microsoft service built for simplifying big data storage and analytics. It is a system for storing vast amounts of data in its original format for processing and running analytics. It is useful for developers, data scientists, and analysts as it simplifies data management and processing. Azure Data Lake offers seamless integration and is the optimal solution to productivity and scalability challenges faced by organizations.

Example: Apache Hadoop

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What is a Data Lake?

Data Lake is a large centralized repository for storing vast amounts of raw data in its original format for future use by a data engineer. A wide range of structured, semi-structured, and unstructured data can be stored in its native form for processing and in-depth analysis. Data Lakes provide unlimited storage space without any restrictions on file size, or data access (including programming, SQL-like queries, and REST calls). It supports metadata extraction, indexing, formatting and conversion, segregation, augmentation, aggregation, and cross-linking.

What is Azure Data Lake?

In April 2015, Microsoft Azure announced Data Lake Service for Enterprise customers. With Data Lake services Microsoft shifted its data storage and analytics service from a basic storage platform to a fully-realized platform for distributed analytics and clustering for HDInsight.

Built on YARN and HDFS Azure Data Lake is a large central storage repository based on Apache Hadoop. It is an alternative to enterprise data silos and holds a massive amount of data in their original format. Data Lake in Azure has the ability to store and analyze large volumes of a variety of data at varying speeds. It is not concerned about the source and purpose of data. It just provides a common repository to perform deep analytics.

Azure Data Lake services are as follows –

1. Azure Data Lake Store:

Data Lake Store is a hyper-scale repository for big data analytics workloads. It allows users to store data irrespective of size and format such as social media content, relational databases, and logs. It provides unlimited storage for unstructured and structured data without any restrictions. An individual file can be a petabyte in size and with no retention policy. It uses the Hadoop Distributed File System (HDFS) for the cloud.

Service Integration for Data Lake Store

  • Data Lake Analytics
  • HDInsight

Microsoft is planning to introduce integration services for Microsoft’s Revolution-R Enterprise, Hortonworks, Cloudera, and MapR, and Hadoop projects such as Spark, Storm, and HBase.

Data Lake Store supports POSIX-style permissions exposed through the WebHDFS-compatible REST APIs. The WebHDFS protocol makes it possible to support all HDFS operations such as read, write, accessing block locations, and configuring replication factors. Besides, WebHDFS can use the full bandwidth of the Hadoop cluster for streaming data.

A new file system-Azure Data Lake Filesystem (adl://) is introduced for directly accessing the repository. Applications and System that are capable of using the new file system gains additional flexibility and performance over WebHDFS. Systems not compatible with the new file system can continue to use the WebHDFS-compatible APIs.

2. Azure Data Lake Analytics:

Azure Data Lake  Analytics is the latest Microsoft data lake offering. It is an in-depth data analytics tool for Users to write business logic for data processing. The most important feature of Data Lake Analytics is its ability to process unstructured data by applying schema on reading logic, which imposes a structure on the data as you retrieve it from its source. The data source could be Data Lake Store or Azure Storage.

It supports U-SQL language, which allows users to run custom logic and user-defined functions. U-SQL provides more control and scalability over jobs. Data Lake Analytics executes a U-SQL job as a batch script, with data retrieved in a rowset format. If the source data is in files, U-SQL schematizes the data upon extraction. 

3. Azure HDInsight: 

HDInsight is a fully managed Hadoop cluster service that supports a wide range of analytic engines, including Spark, Storm, and HBase. It is designed to take advantage of the Data Lake Store in order to maximize security, scalability, and throughput. It supports managed clusters in Linux and Windows.

  • Hadoop: HDFS data storage with support for MapReduce and parallel processing.
  • HBase: NoSQL database built on Hadoop for large sets of structured and semi-structured data.
  • Storm: Distributed, real-time computational service for data streams.

U-SQL –

U-SQL is a language that combines declarative SQL with imperative C# to let you process data at any scale.

U-SQL can process unstructured data by applying schema on reading and inserting custom logic. Each query produces a row set and the row set can be assigned to a variable.

The EXTRACT keyword reads data from a file and defines the schema on reading. The OUTPUT writes data from a row set to a file. These two statements use the Azure Data Lake file path.

Example: adl://mystore.azuredatalakestore.net/Samples/Data/SearchLog.tsv

Example Script:

@searchlog =

   EXTRACT

User Id

int,

Start

DateTime

Region

string

Query

string

Duration 

int

URLs 

string

ClickedUrls 

string

    FROM \”/Samples/Data/SearchLog.tsv\”

    USING Extractors.Tsv(); 

OUTPUT @searchlog   

    TO \”/output/SearchLog-first-u-sql.csv\”

    USING Outputters.Csv();

 

This script reads from the source file called SearchLog.tsv, schematizes it and writes the rowset back into a file called SearchLog-first-u-sql.csv

Also Read: HDInsight of Microsoft Azure

Azure Data Lake Architecture:

Azure Data Lake is built on top of Apache Hadoop and based on the Apache YARN cloud management tool. It is Microsoft’s Implementation for the HDFS file system in the cloud. Azure Data Lake is a completely cloud-based solution and does not require any hardware or server to be installed on the user end. It can be scaled according to need.

Azure Storage API and Hadoop Distributed File System are compatible with Data Lake.

Data Lake is compatible with Azure Active Directory and uses it for security and authentication.

Data Lake is designed to have very low latency and near real-time analytics for web-analytics, IoT analytics, and sensor information processing.

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Azure Data Lake Architecture

Data can be gathered from any sources like social media, website and app logs, devices and sensors, etc. and can be stored in the near-original format.

Related Article: Azure Data Factory Tutorial

Difference between Data Warehouse and Data Lake:

Data Warehouse

 

Data Lake

Structured and Processed

Data

Semi-structured, unstructured and Structured

Schema on write

Processing

Schema on reading

Expensive

Storage

Low cost

Less agile and fixed-configuration

Agility

Highly agile and fully configurable

Mature

Security

Mature

Business professionals

Users

Data Scientists

 

Microsoft Azure Interview Questions

 

Data Lake Security:

Data Lake Security includes:

  • Authentication
  • Authorization
  • Network isolation
  • Data protection
  • Auditing

Data Lake authentication uses the azure active directory for authentication of users and enforcing policies. 

Authorization and access control are stored separately in Data Lake and using below settings

  • Role-based access control (RBAC) provided by Azure for account management.
  • POSIX ACL for accessing data in the store.

Network isolation provides firewalls and defines an IP address range for trusted clients and only these clients can access Data Lake. Data Protection uses Transport Layer Security (TLS) protocol to secure data over the network. Auditing and diagnostic logs are shown in the Azure portal.

Uses of Azure Data Lake

  • General-purpose object storage managed by Azure
  • Streaming and processing of batch workloads.
  • Curation of data by analysts and data engineers for specific needs without making copies.

Benefits of using Azure Data Lake:

  • Highly scalable and flexible as it is housed on the cloud.
  • Allows streamlining data storage for all enterprise needs.
  • Large scale data can be processed simultaneously providing quick access to insights
  • Data Lake stores everything like logs, XML, multimedia, sensor data, binary, social data, chat, and people data.
  • No limit on data storage and file size.
  • Supports heavy analytics workloads for in-depth analytics.
  • It supports schema-less storage whereas the data warehouse does not.
Azure Data Lake Storage Gen2

Built on Azure Blob, the Azure Data Lake Storage Gen2 offers capabilities like file system semantics, directory, file level security, low-cost, tiered storage, high availability/disaster recovery and scalability. Its set of capabilities consists of the best features from Azure Blob storage and Azure Data Lake Storage Gen1. 

Data Lake Pricing:
  1. Data Lake Store:

Pay-as-you-go

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Usage

Price/Month

First 100 TB 

Rs. 2.58 per GB 

Next 100 TB to 1,000 TB 

Rs. 2.52 per GB 

Next 1,000 TB to 5,000 TB 

Rs. 2.45 per GB 

Over 5,000 TB

Custom by contacting Microsoft

 

Monthly commitment packages

Committed Capacity

Price/Month

Savings over pay-as-you-go

1 TB 

Rs. 2,313.37 

12% 

10 TB 

Rs. 21,150.80 

19% 

100 TB 

Rs. 1,91,679.13 

27% 

500 TB 

Rs. 8,79,080.13 

31%

1,000 TB 

Rs. 17,18,502.50 

33%

Over 1,000 TB

Custom by contacting Microsoft

 

 

Price for the transaction:

Usage

Price

Write operations (per 10,000)

Rs. 3.31

Read operations (per 10,000)

Rs. 0.27

Delete operations

Free

Transaction size limit

No limit

 

  1. Data Lake Analytics:

Pay-as-you-go

Usage

Price

Analytics Unit

Rs. 132.20/hour

 

Monthly Committed Price

Included Analytics Unit Hours

Price/Month

Savings over Pay-As-You-Go

100

Rs. 6,610 

50%

500

Rs. 29,744 

55%

1,000

Rs. 52,877 

60%

5,000

Rs. 2,37,947 

64%

10,000

Rs. 4,29,626 

67%

50,000

Rs. 19,16,792 

71%

1,00,000

Rs. 34,37,005 

74%

> 1,00,000

Custom by contacting Microsoft

 

 

Microsoft Azure Data Lake Architecture is helping data scientists, engineers, and analysts by solving much of their big data dilemma. This scalable cloud data lake offers a single storage structure for multiple analytic projects of different sizes. Our online certification helps you learn Azure Data Lake from basic to advanced levels.

If you interested to learn Microsoft Azure Data Lake and build a career in Cloud Computing Technology? Then check out our Azure Certification Training Course at your near Cities.

Microsoft Azure Course BangaloreMicrosoft Azure Course HyderabadMicrosoft Azure Course PuneMicrosoft Azure Course DelhiMicrosoft Azure Course ChennaiMicrosoft Azure Course NewyorkMicrosoft Azure Course WashingtonMicrosoft Azure Course DallasMicrosoft Azure Course Maryland, Microsoft Azure Training VirginiaMicrosoft Azure Training Pennsylvania

These courses are incorporated with Live instructor-led training, Industry Use cases, and hands-on live projects. This training program will make you an expert in Microsoft Azure and help you to achieve your dream job.

 

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Azure Study Guide

Introduction To Microsoft Azure App Service

Introduction to Azure App Service:

Azure app services are Platform as a Service (Paas) model in the Azure cloud platform that enables you to focus on your business logic while Azure takes care of the infrastructure to run and scale your apps. It is a fully managed to compute platform that is optimized for hosting websites and web applications.

  • Supports Windows and Linux platforms
  • Built-in auto scale and load balancing
  • High availability with auto-patching
  • Continuous deployment with Git, Team Foundation Server, GitHub, and Visual Studio Team Services
  • Supports WordPress, Umbraco, Joomla! and Drupal

Azure’s most of the PaaS front end is handled by Azure App Service, a bundle of tools and services that support mobile, web, and API integrations. Azure App Service is an integrated service that enables you to create web and mobile apps for any platform or device. It integrates easily with SaaS solutions (Office 365, Dynamics CRM, Salesforce, Twilio, etc), Easily connect with on-premises applications (SAP, Oracle, Siebel, etc), and easily automate businesses processes while meeting stringent security, reliability, and scalability needs.

If you would like to Enrich your career with a Microsoft Azure certified professional, then visit Mindmajix – A Global online training platform: “Azure Training”  Course.  This course will help you to achieve excellence in this domain.

Azure App Service:

Azure app service includes 4 application development and hosting environment.

1. Web App
2. Mobile App
3. Logic Apps
4. API Apps

The web app was previously known as azure websites. Mobile apps were previously known as azure mobile services. Azure combines them and the introduced app serves as a single platform for building multi-device multi-platform apps. The new Logic App introduces a serverless architecture for various services to be integrated into an app without writing codes and The API App has built-in connectors .that makes it easy to build logic workflows. It integrates with popular SaaS and on-premises applications like Office 365, SalesForce, Dynamics, OneDrive, Box, DropBox, Twilio, Twitter, Facebook, Marketo, and more. Microsoft has connectors for these services or if you wish you can build your own connectors.

Related Article: Overview of Azure Logic Apps

Azure’s primary motive behind the inclusion of all these into app service is to introduce a flexible pay-as-you-go model and enhanced workflow support.

Apps are automatically managed by app services and run in isolated VMs. App Service has an auto-scaling feature so when your app usage increases app service will automatically add resources so that there will be no downtime or latency.  

App Services Architecture:

Azure App service runs on the top of Microsoft’s PaaS offering. Applications can be containerized and packaged to be scalable and platform-independent. 

Microsoft provides built-in connectors, Code integrations, CDN, and default azurewebsites.net domain for all app service applications. App service applications and all resource manager objects for your apps like database, application insights can be grouped into a resource manager and deployed into an azure region near to your customer base.

App service provides CDN for cross geographic delivery of your application and automatic backup, application firewall, and health and performance monitoring tool. It accepts SSL for security.

Depending on plan and pricing app service supports automatic or manual scaling, container isolation for high security.

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App Service Pricing:

App service features no upfront cost and you only billed for a whole minute (Extra seconds are free). The app service has six billing plans.

1. Free
2. Shared
3. Basic
4.Standard
5. Premium
6. Isolated

Free, Shared (preview) and Basic plans provide different options to test your apps within your budget. Standard and Premium plans are for production workloads and run on dedicated Virtual Machine instances. Each instance can support multiple applications and domains. The Isolated plan hosts your apps in a private, dedicated Azure environment and is ideal for apps that require secure connections with your on-premises network or additional performance and scale.

There are two types of SSL connections supported by the Azure app service.

1. Server Name Indication (SNI) SSL Connections
2. IP Address SSL Connections. 

SNI-based SSL works on modern browsers while IP-based SSL works on all browsers.

SNI-based SSL connection is free.

Standard and Premium service plans include the right to use one IP SSL at no additional charge. 

Free and shared service plans do not support SSL. 

Customers can purchase custom domains and assign them to their Azure services like Web Apps or Azure Virtual machines. Custom domains can be managed within the Azure portal. The top-level domains that will be available are com, net, co.uk, org, nl, in, biz, org.uk and co.in.

Azure App Service Best Practice:

Co-location:

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If your web app communicates to a database that is located in a different region or your app resource group dependent on another DB server in another Azure region you may face below problems.

1. Latency in communication between database and application.
2. More data charge for outbound traffic.

To avoid such things it is recommended to use a resource group comprising your app and database in the same region unless you have no specific business need.

Socket Resource Exhaust:

If your app uses client libraries, which have not implemented the reuse of TCP connection then you might face outbound TCP socket exhaust. In such a case, you must use HTTP-keep-alive.

With node js apps you must use http-keep-alive by using npm module specific for this in your code.

Application Backup Configuration:

It is important to set up an automatic backup of your application in the cloud. Using the right configuration in storage and database, you can enable automatic scheduled backup for your application.

Next-Gen Web App Firewall:

Although Azure provides default security and policies, you should use a web app firewall to protect user data and application data. In-app service, setting you should configure inbound and outbound IP for access control.

App Service for Web Apps:

Previously known as azure websites, App service for Web Apps now supported all features with new features like application insights and auto-scaling, auto back up and new programming languages.

* Code support: ASP.Net, Java, Python, Node.JS, PHP
* Built-in auto-scaling (scale up and scale down) 
* Deployment slots: Local git, GitHub, Visual Studio team services, one drive, FTP, bitbucket
* Hybrid setup with VPN support to on-premises servers.
* Orchestrate staged deployment and supports in a test environment.
* Web Job support for running tasks in the background.

Web apps come with default azurewebsites.net domain but you can configure your own domain in the portal.

Web apps can use Azure storage and database. Web apps support both static and dynamic sites.

App Service for Mobile Apps:

App service for mobile apps features mobile app, notification hub, mobile engagement, application insights and a 3rd party vendor provided AI Mobile acceleration service. 

With mobile apps, you can create a highly scalable backend for your mobile app and maintain high user transactions. Notification Hub allows you to create smart and engaging push notifications tailored for different customers and mobile engagement. It is a SaaS-based, data-driven user engagement platform. It enables real-time fine-grain user segmentation, app user analytics, and contextually-aware smart push notifications and in-app messaging across all connected devices.

Real-time analytics for app usage

Process big data and events collected from app users and behaviors. generate analytics to drive more usage and application improvements.

Value-added push and communications platform

Works with native push notification and sends a valuable and interesting notification to increase app engagement.

Open APIs and ease of integration

Open API integration lets you combine data from CRM and CMS.

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Azure takes customer data seriously and does not sell to any third party.

Mobile app in azure app service has the following features:

  • Integrate with SQL, Oracle, SAP, MongoDB, and more.
  • Make your app work offline and sync.
  • Connect to on-premises data.
  • Leverage enterprise single sign-on with Active Directory.
  • Integrate with social providers like Facebook, Twitter, and Google.
  • Broadcast push notifications across platforms, with customer segmentation.
  • Gain insights into mobile analytics.
  • Auto-scale to millions of devices.

App Service for Logic Apps:

Logic Apps allow developers to design workflows that articulate intent via a trigger and series of steps, each invoking an App Service API app whilst securely taking care of authentication and best practices like a durable execution. 

Logic Apps can be designed end-to-end in the browser. Start with a trigger – from a simple schedule to whenever a tweet appears about your company. Then orchestrate any number of actions using the rich gallery of connectors. Logic Apps are part of the App Service suite and designed to work with API apps; you can easily create your own API app to use as a connector.

App Service for API Apps:

Microsoft Azure App Service API Apps offers secure and flexible development, deployment, and scaling options for any sized RESTful API application.

Easily create and consume API and build custom API for your data and application.

  • The fastest way to build for the cloud
  • Provision and deploy fast
  • Simple access control and authentication
  • A secure platform that scales automatically
  • Great experience for Visual Studio developers with automatic SDK generation
  • Open and flexible for everyone
  • Monitor, alert, and autoscale (preview)

Microsoft Azure Tutorials

App Service Tutorial:

Building and deploying a PHP web app in Azure:

PHP is a popular server-side web scripting language and can be used to build a dynamic web application. Azure supports PHP and you can create a web app in PHP in-app service. In this tutorial, we are going to build a web app using PHP and deploy it to Azure app service using Git.

Prerequisites:

1. Install PHP
2. Install Git

Create a git folder and initialize it with README.

Create a PHP file in notepad and save it as index.php

<!--?php
echo "Hello World!";
?-->

Then run the PHP built-in server to run the file locally: php -S localhost:8080

Local Host

Go to your Azure portal and go to Web+Mobile then create a web app.

Microsoft Azure Portal

In the next step, create your app.

1. Enter application name
2. Choose an existing subscription. If you do not have any subscription, you have to sign up for one.
3. Create a resource group for your app or you can choose an existing resource group.
4. Choose the OS you want to use.
5. Select your app service plan 
6. Option to use application insights.
Then click the create button. It will start the deployment.

                          Creation Of Web App

In this way, you can use the graphical user interface and configure the run time and deployment model.

Using Azure CLI:

1. open cloud shell.
2. create a myAppservicePlan with az web app create command.

az webapp create –resource-group myResourceGroup –plan myAppServicePlan –name –runtime \”PHP|7.0\” –deployment-local-git

Create a user for web app

az webapp deployment user set --user-name --password 

Local git is configured with url of \’https://@.scm.azurewebsites.net/.git\’

{
 "availabilityState": "Normal",
 "clientAffinityEnabled": true,
 "clientCertEnabled": false,
 "cloningInfo": null,
 "containerSize": 0,
 "dailyMemoryTimeQuota": 0,
 "defaultHostName": ".azurewebsites.net",
 "deploymentLocalGitUrl": "https://@.scm.azurewebsites.net/.git",
 "enabled": true,
 < JSON data removed for brevity. >
}

It will give a JSON result that now your web app has been created and set to PHP runtime with deployment method is local Git.

Now add azure remote to git so you can push your source to app source code.

git remote add azure

Push your master branch to an azure app. Use the following command

git push azure master

You will get a result like this

Counting objects: 2, done.
Delta compression using up to 4 threads.
Compressing objects: 100% (2/2), done.
Writing objects: 100% (2/2), 352 bytes | 0 bytes/s, done.
Total 2 (delta 1), reused 0 (delta 0)
remote: Updating branch \'master\'.
remote: Updating submodules.
remote: Preparing deployment for commit id \'25f18051e9\'.
remote: Generating deployment script.
remote: Running deployment command...
remote: Handling Basic Web Site deployment.
remote: Kudu sync from: \'/home/site/repository\' to: \'/home/site/wwwroot\'
remote: Copying file: \'.gitignore\'
remote: Copying file: \'LICENSE\'
remote: Copying file: \'README.md\'
remote: Copying file: \'index.php\'
remote: Ignoring: .git
remote: Finished successfully.
remote: Running post deployment command(s)...
remote: Deployment successful.
To https://.scm.azurewebsites.net/.git
  cc39b1e..25f1805  master -> master

After you see deployment successful, you can browse your app with the URL.

Make Changes and Redeploy:

You can change the local copy of your source code and redeploy it to Azure using git. However, before you redeploy it is suggested that you run your app locally and check for issues.

Change the index.php and save it.

echo \”Hello Azure!\”;

Open terminal and run git commit and git push command to Azure.
git commit -am \”updated output\”
git push azure master

Again, you will see the result and after successful deployment, you can browse your app in a browser.

If you are interested to learn Azure and build a career in Cloud Computing? Then check out our Microsoft Azure Certification Training Course at your near Cities

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Azure Study Guide

Server-Less Architecture In Azure| Microsoft Azure

Server-less architecture is event-driven stateless compute containers that are heavily relied on third party services. These third-party services are known as Backend as a Service (BaaS) or Mobile Backend as a Service (MBasS) or custom code that\’s run in ephemeral containers (Function as a Service or \”FaaS\”). For example, you developed a mobile app whose backend and database system is not designed by you but you used a ready to be deployed backend system like parse or firebase.

The term serverless may be confusing. The application has a server hardware and server process which running somewhere else and managed by third-party vendors. Serverless in this scenario that the organization is not looking for hardware and the server-side code itself.

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Let\’s see an example of an eCommerce app. 

Traditional Azure Architecture:

Traditional ArchitectureServer-less Architecture:

 

Server-less architecture

Description:

In serverless architecture instead of writing our own authentication logic and managing the data, we use a third-party service like Facebook or Google authentication.

Use a third-party database as a service product in cloud-like firebase or amazon dynamo DB or azure cosmos DB to create our product database.

Use a cloud search functionality inside your application to allow users to search and discover products.
An event-driven purchase function that writes successful purchase to a purchase database.

Microsoft Azure Tutorials

Evolution of Serverless Computing:

Server-less computing supports multiple languages and abstracts the underlying infrastructure from the developer so that they can focus on building the business logic and rich user experience. Resources are charged for the time they are being used. In serverless applications are broken into small specific core components.

Traditional Architecture

Server-Less Architecture

Serverless computing came after the penetration of cloud computing into development. Serverless computing eliminates the hassle of setting up a server, database, and server-side code writing. Instead, we have APIs we use for interacting with the database and servers.

In the cloud and with a third party BaaS provider we can have a lot of connectors and predefined functions so the developer needs not write their own code to connect with services. Instead, they write functions that trigger in response to an event. 

These functions are evolved as Function as a Service (FaaS). AWS Lambda and Azure Functions are two top Server-less architecture.

Server-less Computing in Microsoft Azure:

Azure Functions is a serverless architecture inside the Microsoft Azure cloud computing platform. It allows writing a small piece of code (function) in any supported programming language like PHP, JavaScript, bash, C#, etc and runs them inside a container.

Azure functions app can connect into azure cosmos DB storage and external applications like sendgrid, dropbox, etc. through connectors.

Azure Functions

Azure Server-less function app stack: 

                     Azure Server-less Functions 

In server-less stack, functions are bound to specific events and triggered when necessary. Example events are

  • HTTP / HTTPS Request
  • Message Queue Trigger
  • File / Storage Trigger
  • Timer / Schedule

Azure Functions

Performance of Server-less architecture:

Server-less computing abstracts the underlying infrastructure from the developer so the developer can focus on building the business logic. The performance of server-less computing depends on the configuration and client-side logic and performance of third-party connectors.

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Azure service plans and data center regions also affect the scalability of function apps.

Azure Interview Questions

Below are the performance figures of Azure Serverless architecture:

  • Scalability:  Manual Scaling in App Service plan and Auto Scaling in Consumption plan
  • Maximum numbers of function:  Unlimited functions
  • Concurrent executions: No limit
  • Max Execution: 300 Seconds
  • Supported language: C#, JavaScript, F#, Python, Batch, PHP, PowerShell
  • Dependencies: Nuget, npm
  • Deployment slots: Visual Studio Team Services, OneDrive, Local Git repository, GitHub, Bitbucket, Dropbox, External repository
  • Environment variables: App settings and ConnectionStrings from App Services
  • Versioning: Cloud Source, Branch/Tag
  • Events: Blob, EventHub, Generic WebHook, GitHub WebHook, Queue, Http, ServiceBus Queue, Service Bus Topic, Timer triggers
  • HTTP(S) invocation: HTTP(S) Trigger
  • Orchestration logging: Azure Logic Apps
  • Monitoring: App Service Monitoring and Application Insights.

Advantage of Server-less Architecture:

Pay for what you use: in server-less computing you only pay for the runtime usage and resource consumption of your function. You do not need to pay for idle resources. 

Scalability: In server-less computing the backend service provider will scale the infrastructure according to load. Developers are no need to write code for scalability or make any provision for  resource allocation.

Rapid Development and Iteration: In server-less computing each functions are single threaded. single threaded functions make debugging easy and deployment easier. The development team are no longer dependent on IT team and deployment happens directly from codebase. CI and CD makes rapid development fast.

Less System Administration: In server-less computing servers are managed by the third party service provides. Developers need not manage and provision servers and infrastructures. No patching and server updates.

Developer Productivity: As the underlying infrastructure is abstracted from developer, they can focus on writing the efficient business logic.

Server-less computing decreases the complexity of the application thus making deployment easier.

Azure functions:

Azure Functions is a serverless architecture inside the Microsoft Azure cloud computing platform. It allows writing a small piece of code (function) in any supported programming language like PHP, JavaScript, bash, C#, etc and run them inside a container.

Azure functions app can connect into azure cosmos DB storage and external applications like sendgrid, dropbox, etc. through connectors.

Azure functions are event-driven and can perform multiple tasks like

  1. Time-based processing – Azure functions support event-based processing. It makes time-based processing easy and a function can be written to perform a job in a specific time interval.
  2. Azure service event processing – We can write a function app that detects azure service event like reading a log file in a storage blog, transform it into SQL database, and send a notification to the user.
  3. SaaS event processing – SaaS event processing like saving a file to one drive from an email attachment or sending a mail when a new file is added.
  4. Web and mobile application backend – Azure functions can power application backend based on user data and events triggered.
  5. Real-time stream processing – Azure stream analytics can receive a message from the Internet of things devices and then process them to create records on SQL azure tables. these records are used to apply analytics on the internet of things data.
  6. Real-time bot messaging – Azure functions can be integrated with azure bot framework to process a bot behavior using a webhook.

Related Article: Azure Stack – Cloud Services

Advantages:

  • Azure Functions are cheaper as compare to other cloud provider’s server-less stack. Azure Functions are only billed for the active consumptions of resources. Cost is based on execution time and memory usage for each function invocation.
  • Azure Functions are simpler and contain less amount of codes. These are single-threaded and easy to debug.
  • Azure Functions are scalable with very minimal configuration.
  • Azure Functions have no limit on the number of functions you can write per project and supports multiple languages than another cloud.

Disadvantage:

  • Vendor lock-in is a common problem in azure functions serverless computing. The vendor will have complete control of the run time environment.
  • Migrating Azure function code to another environment is difficult and often you have to write the code from scratch.
  • Testing and authoring of azure functions outside the azure portal are difficult.

Logic Apps:

Logic apps are managed services that let developers create business apps without writing code. Logic apps provide a building block based on IFTTT (If this then that). Logic apps provide connectors to integrate other third-party services like twitter, sendgrid, dropbox, etc. It is used to automate the workflows and write a custom application for business. 

Related Article:  Azure Analysis Services

Applications, data, and devices can be connected in the cloud or in an on-premises environment with creating logic apps that perform simple automated tasks like saving an attachment to one drive from outlook or send an email when a new tweet is created.

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Logic apps perform an action based on an event or control flow. Azure portal has thousands of predefined templates and connectors to create Logic apps.

Advantage:

  • The logic apps have no upfront or ongoing costs. Logic apps are charged for only resource consumption and hence the cheaper solution.
  • Logic apps require no coding and with a lot of connectors available, it is very easy to write and deploy logic apps.
  • You can create your custom connector with REST API.
  • Logic apps provide inbuilt diagnoses and monitoring each logic app at each step.
  • Logic apps also support continuous integration and continuous deployment.

Disadvantage:

  • Not easy to retrieve information if something goes wrong
  • Formatting data in the web viewer
  • Logic apps are still in preview mode.

Azure Service Fabric:

Azure Service Fabric is a distributed systems platform for packaging, deploying and managing microservice that are scalable and reliable. Service fabric also used for native cloud application development.

Service fabric Services includes:

  • The failover manager ensures availability by shifting resources within the cluster including when resources are added or removed.
  • The cluster manager interacts with the Failover manager to ensure application and service constraints are not violated.
  • Naming Service provides name resolution services to ensure all services within the application are accessible.  Since the workloads are dynamic, client applications are not expected to understand what underlying infrastructure is supporting a particular service.  The Naming Service will facilitate routing between clients and the underlying service.
  • Filestore service provides local data and assembly persistence across nodes in the service.

Azure Service Fabric

In-Service Fabric application can be deployed in two ways.

  1. Stateless
  2. Stateful

Application Life Cycle Management in Service Fabric:

Service Fabric supports application life cycle management, continuous integration and continuous deployment of cloud applications and containers. 

Service Fabric can be integrated with CI/CD tools like Visual Studio Team Services, Jenkins, and Octopus Deploy.

Service Fabric enables simpler application administration, monitoring and patch update. Most applications contain both stateless and stateful microservices and service fabric enables the deployment of multiple instances of the application along with the container and run-time stack.

Related Article: Azure Monitor

Benefits of Service Fabric:

  • Using service fabric, we can deploy our applications in the azure or on-premises data centers without any code changes. 
  • Service fabric programming model and containers allow developers to write scalable apps that are composed of microservices.
  • Service fabric simplifies the application design and assures high availability
  • Manage the lifecycle of your applications without any downtime, including breaking and nonbreaking upgrades.
  • Scale-out or scale in the number of nodes in a cluster. As you scale nodes, your applications automatically scale.
  • Monitor and diagnose the health of your applications and set policies for performing automatic repairs.
  • Service fabric can be run in Linux and supports multiple languages.

Disadvantage:

  • You have to pay extra for using service fabric.

Azure Batch:

Azure Batch is a platform service for running large-scale parallel and high-performance computing (HPC) applications efficiently in the cloud. It schedules high compute-intensive workloads to be performed on a managed collection of virtual machines. It can automatically scale up or scale down resources as required.

Azure batch automates the job. We can easily define compute resources to execute the application in parallel. It is managed service for batch processing of a high volume of data.

Azure Batch

Related Article: A Complete Guide On Microsoft Azure Batch

Example: Video file transcoding.

Let assume we have to transcode large numbers of video files. We upload and configure our application in azure batch. 

  • Upload the input video files to the Azure storage account.

  • Create a Batch pool of compute nodes in your Batch account with properties such as node size, Operating system, and location of the storage account.

  • Create a Batch job to run the workload on the pool of compute nodes and associate it with a Batch pool.

  • Add tasks to the job

  • As the tasks run, you can query Batch to monitor the progress of the job and its tasks.

  • As the tasks complete, they can upload their result data to Azure Storage. You can also retrieve files directly from the file system on a compute node.

  • When your monitoring detects that the tasks in your job have completed, your client application or service can download the output data for further processing or evaluation.

Advantage:

  • High performance and highly scalable

  • Fully managed service

  • You only pay for the resources you use

  • Azure batch supports Windows and Linux

Disadvantage:

  • Requires platform expertise in setting up tasks

  • More programmatically and less tooling.

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Azure Server-Less Architecture Overview – Infographics

Azure Server-Less Architecture Overview - Infograpgics

 

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Overview Of Azure Logic Apps, Architecture, Integration – MindMajix

Introduction to Azure Logic Apps

LogicApps are a managed service that provides the Lego blocks of Azure serverless architecture that you can use to create, develop and deploy cloud-based integrations and workflows. The developer merely strings together off-the-shelf, ready-made components with as little effort as possible. The aim is to go from development to production in a few simple, reproducible steps. Microsoft is investing strongly in this technology, introducing many new connectors in line with the demands of the integration requirements of enterprises. Logic App service is a Code-less integration service for communicating with different services or platforms. With Visual business flows, B2B integration, and developer friendliness, Microsoft makes themselves a strong contender in the cloud computing.

What are Azure Logic Apps?

Logic Apps provide a way to simplify and implement scalable integrations and workflows in the cloud. It provides a visual designer to model and automates your process as a series of steps known as a workflow. Logic Apps allow developers to design workflows that articulate intent via a trigger and series of steps, each invoking an App Service API app whilst securely taking care of authentication and best practices like durable execution.

If you would like to get certified and make a career on these platforms, then visit Mindmajix A Global training online platform :  \”Microsoft azure training\” These courses will help you to become a professional in these domains.

Related topic: Azure Service Bus

Logic Apps is a fully managed iPaaS (integration Platform as a Service) allowing developers not to have to worry about building hosting, scalability, availability, and management. Logic Apps will scale up automatically to meet demand.

Workflow of Azure Logic Apps

Azure logic apps architecture

IFTTT (‘If This Then That’) architecture is basically used in logic apps which allows the developer to create simple conditional statements and event triggers without writing complex codes thus reducing the development time and effort.

Azure LogicApps Components

Azure Logic apps have four key components.

1. Workflow 
2. Managed Connectors
3. Triggers 
4. Actions

Workflow – Logic Apps provides a graphical way to model your business processes as a series of steps or a workflow.

Managed Connectors – Logic apps need access to data and services. Managed connectors are created specifically for connecting to and working with data.

Triggers – Some Managed Connectors can also act as a trigger. A trigger starts a new instance of a workflow based on a specific event, like the arrival of an e-mail.

Action – the step after a trigger that makes something happen, such as sending an email

More about connectors

Connectors are an integral part when creating logic apps. Connectors allow connecting to 3rd party application to manage data and working with it. On-premise applications can be sync to cloud applications using connectors.

For more information click: Azure Application Insights

Standard connectors: Automatically available and included when you use logic apps. Some examples include Service Bus, Power BI, Oracle Database, OneDrive, Dropbox. Google Drive, Outlook 365, twitter, Salesforce, Sharepoint online and many more.

Integration account connectors: Available when you purchase an integration account. Using these connectors, you can transform and validate XML, process business-to-business messages with AS2 / X12 / EDIFACT, and encode and decode flat files. If you work with BizTalk Server, then these connectors are a good fit to expand your BizTalk workflows into Azure.
BizTalk Server also has a Logic Apps adapter that includes receiving from a logic app and sending it to a logic app.

Enterprise connectors: Includes IBM MQ and SAP. Available at an additional cost.

Connectors as triggers and actions

A trigger starts or runs an instance of your logic app. Some connectors provide triggers that notify your app when specific events happen. For example, the FTP connector has the OnUpdatedFile trigger that starts your logic app when a file is updated. 

Logic apps include the following types of triggers: 

Poll triggers: These triggers poll your service at a specified frequency to check for new data. When new data is available, a new instance of your logic app runs with the data as input. 

Push triggers: These triggers listen for data on an endpoint, or for an event to happen, then triggers a new instance of your logic app.

Recurrence trigger: This trigger instantiates an instance of your logic app on a prescribed schedule.

Custom Connector

Connectors are web APIs that use REST for pluggable interfaces, Swagger metadata format for documentation, and JSON as their data exchange format. Because connectors are REST APIs that communicate through HTTP endpoints, you can use any language, like .NET, Java, or Node.js, for building connectors. To create a custom connector, you must have a REST API.

Custom Connector Created for Logic Apps

* Registered as Logic Apps Connector resources in Azure.
* Appear with icons alongside Microsoft-managed connectors in the Logic Apps Designer. 
* Available only to the connectors\’ authors and logic app users who have the same Azure Active Directory tenant and Azure subscription in the region where the logic apps are deployed.

Microsoft Azure Interview Questions and Answers

Azure LogicApps Advantages

Easy to use design tools – Azure Logic Apps can be designed end-to-end in the browser. Start with a trigger – from a simple schedule to whenever a tweet appears about your company. Then orchestrate any number of actions using the rich gallery of connectors.

Compose SaaS easily – Even composition tasks that are easy to describe are difficult to implement in code. Logic Apps make it a cinch to connect disparate systems. Want to create a task in CRM based on activity on your Facebook or Twitter accounts? Want to connect your cloud marketing solution to your on-premises billing system? Logic apps are the fastest, most reliable way to deliver solutions to these problems.

Extensibility baked in – Don\’t see the connector you need? Logic Apps are part of the App Service suite and designed to work with API apps; you can easily create your own API app to use as a connector. Build a new app just for you, or share and monetize in the marketplace.

Real integration horsepower – Start easy and grow as you need. Logic Apps can easily leverage the power of BizTalk, Microsoft\’s industry-leading integration solution to enable integration professionals to build the solutions they need.

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Logic apps have no upfront setup costs. They also have no ongoing costs or infrastructure costs. The pricing works on the basis of consumption. This means that only you are only charged for executed actions. For a DropBox connector that is configured with a “File modified” trigger, there will be no cost unless there are modified files that trigger the LogicApp execution. It’s also extremely scalable because each trigger will instantiate a new LogicApp. Consequently, multiple concurrent triggers will force multiple concurrent instances of the same LogicApp to run: This ensures that everything is processed in a timely manner.

Azure LogicApps Performance

LogicApps are “infinitely scalable”. LogicApps are always ready for execution. 3rd party API) may cause latency issues. some connectors have hard limits on the available requests per second.

HTTP request limits

These limits apply to a single HTTP request or a connector call. 

Timeout

Name Limit Notes
Request timeout 120 seconds An async pattern or until loop can compensate as needed

Message size

Name Limit Notes
Message size 100 MB Some connectors and APIs might not support 100 MB.

Retry policy

Name Limit Notes
Retry attempts 90 The default is 4. You can configure the retry policy parameter.
Retry max delay 1 day You can configure the retry policy parameter.
Retry min delay 5 sec You can configure the retry policy parameter.

Run duration and retention
These limits apply to a single logic app run. 

Name Limit
Run duration 90 days
Storage retention 90 days from the run\’s start time
Min recurrence interval

1 second

For logic apps with an App Service Plan: 15 seconds

Max recurrence interval 500 days

To exceed the limit, we need to contact Microsoft support.

Related Post: Microsoft Azure Application Gateway

Logic App Development

LogicApps provide three development models.

1. Azure portal

In the Azure portal, we can create a logic app. We can switch between the designer and the code view. The Code view comes handy for advanced configurations. The “code-behind” each LogicApp is based on JSON so you can easily choose to create LogicApps using JSON instead of the designer, though this requires intimate knowledge of the LogicApps API.

2. VS 2015 and Later

We can use Visual Studio 2015 and later to develop logic apps. We need some pre-requisites to be installed.

Pre-requisites

* Visual Studio 205 or later
* Azure SDK 2.9.1 or later
* Azure Powershell
* LogicApps Visual Studio Extension

As long as these prerequisites are met, developers can start developing and deploying LogicApps from Visual Studio by selecting the Azure Resource Group template and then choosing the LogicApps template.

choosing the LogicApps template

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Select Azure Template

3. Templates

There are plenty of predefined templates that are available for common business cases, like HTTP Request-Response, that greatly simplifies the work of the development team. Templates are the fastest way to get started with the power of LogicApps.

Check Out  Microsoft Azure Tutorials

Azure Logic Apps Tutorial – Real-time Scenario with example

Microsoft Azure Logic Apps: Syncing your DropBox, OneDrive and Google Drive

Pre-requisites:

* Dropbox account
* Google Account for Google Drive
* Microsoft account for OneDrive

Dropbox
Dropbox is a file hosting service that offers cloud storage, file synchronization, private cloud, and client software. A Dropbox Basic account is free and includes 2 GB of space.

OneDrive
OneDrive is a file hosting service that allows users to sync files and later access them from a web browser or mobile device. Users can share files publicly or with their contacts; publicly shared files do not require a Microsoft Account to access them. OneDrive offers 5 GB of free space to the users.

Google Drive
Google Drive, formerly Google Docs, is a file storage and synchronization service created by Google. It allows users to store files in the cloud, share files, and edit documents, spreadsheets, and presentations with collaborators.

Related Post: A Complete Guide On Microsoft Azure Batch

Creating Logic Apps application in Azure

Visit portal.azure.com and sign in with your credentials. If you don’t have a subscription you can use a trial subscription by verifying your credit card.

Create a new Logic Apps application by clicking on the Logic Apps, it will ask for the following things:

Name: which is giving your application a name

Subscription: right now it is free subscription but it will expire in 30 days, otherwise you need a valid Azure subscription

Resource group: It is basically a logical container that is used to manage various assets/resources that share the same lifecycle, permissions policies. For e.g. all resources for BizTalk could be grouped together and could be given a specific resource group name.

Location: The location of the Datacenter in which your Logic apps application would get deployed and stored

Create a new Logic Apps application

 

Create logic app

After clicking on this application we go-to development tools that contains Logic App Designer, API connection. Now let’s create a template for development, just go to logic app designer and select Dropbox to OneDrive template for syncing:

Logic Apps Designer

After that you need to sign in to both you OneDrive, Dropbox and Google Drive. You will be redirected to respective login pages and you need to allow access permission to azure logic app service.

Access permission to azure logic app service

In Folder I have selected root \’/\’ as I would be dropping a file directly on Dropbox, not in any folder, the frequency could be in minutes, hours or days.

For simplicity, I have used 3 minutes

For OneDrive:

Create file in One Drive

For OneDrive configuration a new folder is created in the root directory with Filename as the same that got uploaded in Dropbox and the same file content.

After doing the configuration of Dropbox and OneDrive lets add Google Drive template of Create file like below:

Google Drive template of Create file

Now we need to add a new step i.e. to create a new file on Google Drive, for that we have template in Logic Apps like below:

create a new file on Google Drive

Now let’s configure this template:

Created a new folder in Google Drive

Folder Path: Created a new folder in Google Drive

File Name & File Content: Select output from Dropbox as the input of Google Drive – Create file action

Now let’s come to the main part of running and testing our solution. Just by clicking the save in logic apps designer will deploy the code and the code would be ready to be run and tested.

MyFile.txt in Dropbox

I have uploaded a new file called MyFile.txt in Dropbox, now after every 3 minutes our code would poll the path configured and pick up MyFile.txt and create a new file in both OneDrive and Google Drive on the locations specified in their respective templates:

A new file is created on OneDrive:

A new file is created on OneDrive

At the same time a new file in Google Drive is also created:

A new file in Google Drive

In the above example when a file is created in your dropbox it will automatically sync to or copied to you one drive and google drive. We see how we can create a logic app without writing any code. The logic app can automate business processes in this way. 

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These courses are incorporated with Live instructor-led training, Industry Use cases, and hands-on live projects. This training program will make you an expert in Azure and help you to achieve your dream job.

 

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Azure Study Guide

AWS Vs Azure (2020)Top 13 Key Comparisons To Choose Best Cloud Service

AWS vs Azure: Which One Would You Choose?

AWS and Azure are the top two cloud platforms available today and the majority of the organizations across the globe and using either of these two platforms. So, before selecting any one of these, it is essential for businesses to get familiarized with their features and services. 

If you are in a confused state to choose between AWS vs Azure for your business requirements, then you are in the right place. 

This blog provides you with an insight into AWS and Azure and compares all their features and services. 

Thus, it gives you a clear idea of which of these cloud platforms is best for your organization.

Before going to the key factors’ comparison part, let us provide a brief introduction to AWS and Azure.

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What is AWS?

AWS is a cloud computing service created by Amazon, and it provides services in the form of building blocks; these services can be used to create and deploy any application in the cloud.

For more information What is AWS

What is Azure?

Azure is a cloud computing service created by Microsoft for deploying, building, testing and managing services and applications through Microsoft data centres. It supports different programming languages, frameworks, and tools including third party and Microsoft-specific software and systems

For In-depth information: What is Azure

In case if you want to become a certified professional on either of these platforms, then visit Mindmajix – A Global online training platform: “Azure Training” and “AWS Training Course.” – These courses help you to achieve excellence in these domains.

Now, let’s dive into the key elements that we are going to consider to make a comparison between Azure vs AWS. 

Here are the top key features that we are going to consider in the comparison between AWS vs Azure:

  1. Content Delivery Network
  2. Storage
  3. Compute
  4. Networking
  5. Database
  6. Security
  7. Cost
  8. Open Source
  9. Compliances
  10. User-friendly
  11. License
  12. Hybrid Cloud Capabilities
  13. Market Share

1. Content Delivery Network: 

A content delivery network [CDN] is a distributed network of servers that delivers efficient content to the users. The content is temporarily stored on servers in the nearest local regions. Now, let\’s check how Azure and AWS are providing the CDN services to their users. 

Azure – Microsoft Azure

Microsoft Azure CDN provides a solution to the developers to deliver fast and high-bandwidth content to users with high security and real-time analytics. It can also speed up dynamic content which cannot be cached by various network optimizations using CDN POPs.

AWS – Amazon Web Services

CloudFront is the Content Delivery Network for AWS which is easy to get started and integrates using Simple storage service. AWS is providing CDN for less price and it suits best for startups.

Related blog: Clean Up Process In AWS

2. Storage:

In terms of storage, AWS has a better offering than Azure. 

Azure: Azure supports import-export, site recovery and additional backup for archival and retrieval options. The primary storage of the Azure is Microsoft\’s cloud, and it is designed to resolve your current data storage issues. Azure stores your temporary data using disk drives, blocks, and file system services.  

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AWS: AWS has a Simple Storage Service which can be used for object storage and delivery. It allocates the storage once an instance gets started and the storage gets destroyed when the instance is terminated. 

It has blocks storage (same as a hard disk) that can be attached or separated by the instance. Object storage provides S3 and archival data services with Glacier. It fully supports Big Data, relational and NoSQL databases.

3. Compute:  

The next factor is computed or computation services; it is the core feature offered by most cloud computing platforms. 

Azure: Azure is one of the major cloud computing service providers. It provides several infrastructures, including App services, functions, VMs, container services, etc. 

The Azure Marketplace has various templates to choose from like Windows Server, SharePoint Server, SQL Server, Red Hat Linux, Ubuntu, NextGen WebApp Firewall, etc. Users can select the availability region and standard VM size which is available in the Marketplace.

AWS: AWS provides various virtual compute services for effective computing. Some of the leading computing infrastructures include Elastic Compute Cloud or EC2, Elastic Beanstalk, AWS Lambda, etc. 

Amazon Machine Images (AMI) are available in the marketplace to choose between different operating systems. Users can configure their machines with templates and specify memory and cores.

4. Networking: 

Cloud networking is also a part of the cloud computing infrastructure to interconnect with cloud-based applications, solutions, and services. 

Azure: By offering a virtual network (VNET), Azure enables the users to create separate networks as well as route tables, network gateways, private IP addresses, and subnets.

AWS: By offering a virtual private cloud(VPC), AWS allows users to create separate networks, subnets, network gateways, private IP addresses, and route tables under the cloud umbrella.

Both enterprises have solutions to extend the on-premise data centers into the firewall and cloud options as well. 

Check Out AWS Tutorials

5. Database: 

Since databases consist of data in huge amounts and various formats, users are facing problems to access the data. To overcome this, both Azure and AWS provide different kinds of databases to handle both structured and unstructured data.

Azure: Azure uses SQL database, PostgreSQL and MySQL for the relational database, Cosmos database for NoSQL solutions, and Redis Cache for caching purpose.

AWS: AWS uses relational databases for durability, and it supports different database engines like Amazon Aurora, MySQL, Oracle, PostgreSQL, and MariaDB. It also provides DynamoDB for NoSQL solutions and Elastic Cache for caching purposes. 

6. Security: 

Azure and AWS data centers are highly secured firms and strong physical security is present in each data center. Trust agencies certify data security and compliance. 

As cloud providers store sensitive business information, they provide high-end encryption and strong password policies for VMs, Databases, and Applications.

Amazon EC2 uses a modified version of the Xen hypervisor which can significantly improve the performance of virtual machines through Paravirtualization. Besides, access to the CPU comes with separate privileges. 

Azure and AWS use multifactor authentication and mutual SSL authentication. All firewall ports are closed and users must give access to the incoming traffic. Amazon provides the ability to split the levels of access groups. 

7. Cost: 

Comparing the price of AWS and Azure is a complicated thing because computing power units vary between Azure and AWS. 

Azure: Azure has a PAYG (pay as of you-go) but they charge per minute. It provides exact pricing models and short term commitments with monthly plans and prepaid services.

AWS: Similar to Azure, AWS also follows the PAYG model, but they charge per second. Purchase instances are based on the following conditions. Find here how do you pay for AWS?

  • On-demand: They charge for what you use (Instance) without the upfront cost. 
  • Reserved: They charge based on your instance usage. Subscribe to an instance for 1 to 3 years with an upfront cost. 
  • Spot: Customers choose available instances without an upfront cost.

You can use Azure and AWS calculators to estimate the cost according to your cloud requirements. Both vendors are providing trial versions for new users. Try it once and choose the best one which meets your needs.

8. Open source: 

Open source is the essential feature to discuss in Azure and AWS battle. Let us check how they are providing the services.

Azure: Azure provides Windows development tools such as VBS, SQL database and active directories for corporate users. To use the current active directory, a user needs to login to the Azure cloud platform and runs the .net framework on Windows, Linux, and macOS.

AWS: AWS is an excellent option for open source developers as it offers several integrations for various open-source applications.

9. Compliances: 

Azure: Azure provides more than 50 agreements, including ITAR, HIPAA, FIPS, DISA, CJIS, and more. Azure provides security to the data so that it allows authorized persons to access the cloud.

AWS: AWS has a good and long relationship with the government agencies, and they are offering the compliances including CJIS, HIPAA, FIPS, DISA and a lot more. AWS also provides security so that only authorized persons can access the cloud and handle the sensitive agencies\’ information. 

When it comes to security, AWS is neck to neck with the Azure. 

10. User-Friendly: 

Azure:  Azure is the most powerful and user-friendly cloud platform. Many organizations are migrating to this platform due to its ease-to-use nature and quick results. If you are a Windows admin, you can use Azure without learning anything new as it doesn’t require any new installations. You can integrate the Windows server with the cloud instances to create hybrid environments. Azure provides wonderful tools such as SQL database, Active Directory, etc., according to the user requirements.

AWS: AWS is also a user-friendly cloud platform to the users. But, it is a bit complex when compared to Azure because the user needs to learn new system configurations and features. Also, the user needs to install new installations and understand all the settings of a system. 

11. License:

Azure: To get the Azure license, a user needs to meet specific requirements. Azure offers the license mobility only for the qualifying application servers. So, users need to check their server whether it is suitable for mobility to avoid the extra paying. 

Windows Server itself does not satisfy the license mobility eligibility list. So, if you are running the VM on the server, Windows server pays only for the usage of the instance.

Frequently asked AWS Interview Question

12. Hybrid Cloud Capabilities:

Azure: Azure has long supported hybrid cloud services such as Azure Stack, Azure Storsimple and Hybrid SQL Server. These services allow you to bring full Azure functionalities to your own premises data centers using the PAYG model for its public cloud. 

AWS: Amazon recently launched a hybrid cloud service called Snowball Edge. It is a 100TB hard drive and allows the users to move workloads between the company\’s cloud and client data centers. Amazon is still developing its hybrid services. 

Last but not least, let us discuss the market share of Azure and AWS.

13. Market Share: 

According to the Canalys report, the market shares of Azure and AWS are increasing day by day. Canalys chief analyst Edwards has said that “Nowadays, the cloud infrastructure is moving into a new phase of hybrid, where IT was adopting these services to make the integration easier.”

Considering the global cloud infrastructure services market this year, AWS achieved the biggest gain with sales up to US$2.3 billion and Microsoft Azure is in the second position with sales up to US$ 1.4 billion.

Here is a table depicting the comparison between Azure vs AWS in a comprehensive manner: 

Azure AWS
Microsoft Azure CDN provides a solution to the developers to deliver fast and high-bandwidth content to users with high security and real-time analytics. CloudFront is the Content Delivery Network for AWS, which is easy to get started and integrates using Simple storage service.
The primary storage of the Azure is Microsoft\’s cloud which is designed to resolve your current data storage issues. AWS has a Simple Storage Service which can be used for object storage and delivery.
Azure integrates with several infrastructures, including App services, functions, VMs, container services, etc. AWS also integrates with computing infrastructures include Elastic Compute Cloud or EC2, Elastic Beanstalk, AWS Lambda, etc.
Azure offers a virtual network (VNET). AWS provides a virtual private cloud (VPC).
Azure uses SQL database, PostgreSQL, MySQL and a lot more. AWS uses Amazon Aurora, MySQL, Oracle, PostgreSQL, and MariaDB.
Azure uses both multifactor authentication and mutual SSL authentication to avoid unauthorized users.  AWS also uses both multifactor authentication and mutual SSL authentication to avoid unauthorized users. 
Azure has a PAYG (pay as of you-go), but they charge per minute. Amazon also follows the PAYG model, but they charge per second.
Azure provides Windows development tools such as VBS, SQL database and active directories for corporate users. AWS is ideal for open source developers as it welcomes the Linux users and offers several integrations for various open-source applications.
Azure provides more than 50 agreements, including ITAR, HIPAA, FIPS, DISA, CJIS, and more. AWS also provides CJIS, HIPAA, FIPS, DISA, and lot more.
Azure is the most powerful and user-friendly platform to the users. Compared to Azure, AWS is not a user-friendly platform for users.
Azure provides a license to the user when they meet the requirements. AWS provides the new license to users or they can renew the old license.
Azure offers hybrid cloud services such as Azure Stack, Azure Storsimple and Hybrid SQL Server. AWS recently launched a hybrid cloud service called Snowball Edge.
Azure market share value in dollars is US$1.4 billion. AWS market share value in dollars is US$2.3 billion.

 

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These courses are incorporated with Live instructor-led training, Industry Use cases, and hands-on live projects. This training program will make you an expert in AWS and help you to achieve your dream job.

 

Conclusion

We hope this article helps you to understand the key differences between the features and services of “Azure and AWS.” Both Microsoft’s Azure and Amazon’s AWS are powerful & dynamic cloud platforms. 

AWS offers several features, and Azure is still growing and introducing new and exciting features. So it is difficult to pick a clear winner in this battle. Both providers can be used for different parts of operational needs according to your business requirements. 

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Introduction To Azure DevOps And Its Work Process

 Azure DevOps: A detailed and practical guide on DevOps from basics to advance  

Garnering over 15 years of learning and investment in providing tools for supporting software development teams, Azure DevOps is well equipped to handle DevOps. Initially, the visual studio team system was developed into Azure DevOps and released in 2018. Azure DevOps is implemented both on the off-premises as Azure DevOps Server and an online one called Azure DevOps services. But before going on with understanding it, you need to know some basics about DevOps.

What is Azure DevOps – Basics, and Terms

DevOps is a development methodology that is intended to decrease the time of development all while providing the continuous delivery of high-quality software. It is the combination of two words: Software Development (Dev) and IT Operations (Ops).

An exhaustive list of even important terms will take a lot of space. Let us see some of the most important and basic terms used in Azure DevOps projects. Agile: It is also another development methodology of software production. While Agile and DevOps sometimes are used interchangeably, they are not the same. Continuous Integration: It is the technique of continuously merging source code updates into one mainline in order to prevent conflicts. Automated Builds: They enable us to compile all the source files and package the resulting files and producers’ installers. 

DevOps is the combination of practices, tools, and philosophies that enable rapid development and deployment of enterprise and consumer applications. DevOps began with cloud computing, which made it possible to allocate resources quickly and inexpensively. Cloud maximizes the innovations under DevOps by combining development, testing, and deployment tools in the cloud.  Traditionally,  it doesn\’t offer the flexibility that the cloud has to offer while delivering apps that need to be deployed faster and monitored through the development life cycle.

DevOps combines and merges the development team and IT Ops team together to work closely in the application lifecycle. 

 

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Quality Assurance and Security teams are also tightly integrated and work with the DevOps team. Azure DevOps team practices to automate all development and operations processes. In Azure DevOps, the technology stack and tools are reliable, evolve quickly, and help engineers to accomplish tasks quickly. For instance, continuous deployment of code from GitHub of Visual Studio Team Services or provisioning cloud infrastructure for application deployment. Normally these would have required help from other team but as Azure DevOps integrate these into a single unit this increases development velocity.

Azure DevOps Terminology:

Infrastructure AS Code

  1. Continuous Integration: Continuous Integration (CI) is the process of automating the build and testing of code, every time a team member commits changes to version control. CI keeps the master branch clean and creates a development branch. After testing is completed and the pull request is accepted then changes are committed to the master branch.

  2. Continuous Delivery: Continuous Delivery (CD) is the process to build, test, configure, and deploy from a build to a production environment.

  3. Infrastructure as Code: Infrastructure as Code (IaC) is the management of infrastructure (networks, virtual machines, load balancers, and connection topology) in a descriptive model, using the same versioning as DevOps team uses for source code. Infrastructure as Code enables DevOps teams to test applications in production-like environments early in the development cycle.

  4. Micro-services: Microservices are architectural patterns in which applications are divided into smaller components and deployed to perform specific business logic. They communicate over the web using Azure DevOps REST API.

Skills required to learn Azure DevOps:

With the end to end solutions that Azure provides, software development teams can implement the DevOps practices in all the phases of the development. But in order to learn DevOps, developers need certain skills and knowledge. Let us see each of them:

  • Understanding of Major DevOps Tools

This would be an obvious requirement as Azure DevOps is a great collection of products and services that would help you to implement DevOps methodology. So, you need to learn all the major DevOps tools like Jenkins, Docker in order to learn Azure DevOps

  • Software Security Skills. 

Security of the application is given equal importance, if not more than that of working on the application. If hackers can get hold of any system, they can affect the entire network. So, it\’s the responsibility of the developers to write code so that the application is secure.

  • Command of Automation Technologies and Tools. 

Using automation, you can make the development and deployment processes faster. Good knowledge of automation technologies and tools is thus very important for DevOps.

  • Coding and Scripting Skills. 

Coding and scripting skills in many different languages like Ruby, Python, Perl, and others is very important for Azure DevOps developers as they are responsible for writing manual codes or replacing a manual process.

  • Cloud Skills. 

The reason cloud is so important as it provides a good place to backup all the data and also helps in orchestration which offers better and co-ordination in the automation process. It helps in developing and deploying applications faster. 

  • Testing Skills.

Azure DevOps provides powerful services that will help in the testing of applications. In order to use them well, you need a good knowledge of testing and different approaches within it as all reputed companies use different methods of testing.

Benefits of Azure DevOps:

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The main reason that Azure DevOps is so important to learn is that it combines the greatness of the cloud and the process of DevOps. By learning Azure DevOps, you can use it for yourself to build the applications efficiently or else join in an organization that is actively seeking developers who are proficient in Azure DevOps for a great role!

  1. Speed: In DevOps microservices and continuous delivery allowed teams to take ownership of services and then release updates to them faster. 

  2. Rapid Delivery: DevOps combines tools and all processes in the cloud so starting from developing the apps and testing it in the cloud by using automated tools and deploying as soon as code commit happens. This enables rapid delivery.

  3. Reliability: Monitoring and logging practices help you stay informed about performance in real-time.

  4. Scaling: Infrastructure as code helps you manage your development, testing, and production environments in a repeatable and more efficient manner.

  5. Improved Collaboration: With DevOps and a single combined platform Developers and Operation, teams collaborate easily. They are no longer isolated from each other and share responsibilities and feedbacks.

  6. Security: Automated compliance policies, fine-grained controls, and configuration management techniques can define and then track compliance at scale.

Introduction to Azure DevOps:

The Azure platform is full of flexible DevOps workflows. Microsoft provides the following services for DevOps in their Azure cloud platform.

  1. Visual Studio Team Services: Services for teams to share code, track work, and ship software.

  2. Visual Studio Mobile Center: Mission control for your mobile apps.

  3. Visual Studio: Integrated development environment (IDE) from Microsoft.

  4. HockeyApp: Deploy mobile apps, collect feedback and crash reports, and monitor usage.

  5. Xamarin in Azure: Xamarin a framework that enables rapid development and testing of mobile apps in the cloud, xamarin can deploy to multiple platforms from a single codebase and xamarin test cloud can test your application in real devices in the cloud.

 

In addition, the Microsoft Azure cloud platform supports third-party tools like Jenkins, Redhat, Chef, Spinnaker and Terraform.

Related Article: Azure Stack – Cloud Services

Azure On DevOps Infographics:

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Azure On DevOps Infographics

 

Azure DevOps Life-cycle:

Azure DevOps’ life cycle mainly follows the below-mentioned phases:

  1. Write code, Test, and Debug – During the application development stage, the code for Azure applications is most commonly built locally on a developer’s machine. Microsoft has recently added additional services to Azure Apps named Azure Functions. The azure function is a serverless computing feature that enables the developer to build enterprise applications without writing codes and maintain infrastructure.

  2. Visual Studio Team Services and Git are two popular code collaboration tools for Deploying and Monitoring.

  3. Azure App Service for DevOps provides following deployment slots for Continuous integration and deployment of your codebase:

  • OneDrive

  • Dropbox

  • FTP

  • Local git repository

  • Github

  • Visual Studio Team Services

 

Azure DevOps Workflow

If your code is open source and hosted in public Github, you can use Continuous Integration tools like Travis CI or CircleCI for code testing and deployment

Azure DevOps Service Vs. Server

The first main difference is about where the entity operates. In the case of Azure DevOps Services, it operates on the cloud while the Axure DevOps server operates on-premise. As per the scaling and scoping of data, Azure DevOps services provide only two options: for organizations and projects while Azure DevOps server provides three options: projects, project collections, and deployment.

Azure DevOps Pricing:

There are two general kinds of pricing in Azure: Individual services and also taking a user license. For individual services, You can have one free Microsoft hosted CI/CD up to 1800 minutes and also one free self-hosted CI/CD with unlimited time. You can use up azure artifacts of 2GB storage worth. But after the free parallel jobs, you need to pay around Rs. 2500 for Microsoft hosted jobs and around Rs 1000 for self-hosted jobs. You have to pay an amount of around Rs. 150 after the free storage too.

Coming to the user license, there are two plans: Basic plan and Basic plan with testing. The basic plan includes all the basic features and is free for the first five users. After that, you need to pay around Rs.400 per user per month. But for the basic plan with testing, You need to pay an amount of roughly Rs.3500 per user per month from the start.

Azure DevOps Services:

Microsoft Azure DevOps provides a lot of services to aid the development of software in all the phases of its lifecycle including planning the work, collaborating during development and also while building the applications. In order to understand how it does this, we need to discuss its infrastructure.

Azure DevOps Infrastructure in detail:

Azure DevOps infrastructure comes with a lot of components that do specific jobs. Let us discuss each of them in detail. 

Azure Test Plan:

By using Azure Test plans, you can test your application manually and also run exploratory tests. You can perform unit and function tests as well. You also have continuous testing enabled! Also with Azure test plans, you can request, provide, and track feedback.

Azure Board:

Azure boards make the work of tracking tasks, bugs, and features a lot easier. In this, there are three types of work items: Epic work item which tracks features or any requirements, Issue work item which tracks bugs or any small changes and task work item which tracks works that are smaller than that of tracked by issue.

Azure Repo / Repos:

Azure DevOps Repo or Repos is a collection of many version control tools that could be used to manage your code. It provides mainly two types of version control software: GIT repo and TFVC (Team foundation version control). 

Azure DevOps Pipeline:

The Azure DevOps pipeline can be used in creating and testing your code automatically and also in giving access to other users. As it is a combination of Continuous Integration and continuous delivery, it can produce consistent and good quality code with using both of them.

For public projects, Azure pipelines are completely free while for private projects, you can get 30 hours of it free for every month. You might be wondering what azure pipelines are, we saved it for a later discussion. 

Azure Artifacts:

To say simply, artifacts are executable files and thus contain only machine instructions rather than human-readable higher-level code. A Feed is something like a container of packages that helps in consumption and publishing. Now coming to Azure artifacts, it is a store that contains all your artifacts which you have produced during development and deployment. They provide a fast and secure feed of binary packages that are also easy to use. You can also use multiple feeds for organization and control of access to your packages.

Azure DevOps Hands-on:

Now that you have understood many aspects of Azure DevOps, let us have a small hands-on work by creating a DevOps organization and using the services of Azure DevOps.

Start by going to the Azure portal and searching for DevOps. Then click on Create a new organization and then continue on giving the required details until you created the project. You can add new members to this project by going to project settings and then security. Here, you need to click on the team and then on members where you can add.

Next, create a Git repository and push it to azure server. You can use this for collaborating with other members of the team. You can see all the repos and commits under the repos in the azure repos. If you want to add new work, go to boards and select a new work item. You can also use the boards to assign a new task to the user from here.

Now go to the pipelines and start a new one specifying the resource where your code is. After that, your default pipeline would be created. You can write down any tasks you want to perform and click run to execute them.

Another main thing that is remaining is testing. There are different ways of testing, let us go for URL tests for now. After loading and creating a new test, all you need to do is to fill all the required details and run the test!

That briefly covers basic aspects of all there is in Azure DevOps!

DevOps Online Training Certification:

Learning Azure DevOps is quite simple with the right guidance with the right Azure DevOps Tutorial. We provide the right course for you! Our training is completely online and you will receive a certificate upon completion which can be extremely useful!

Bottom Line

With the practical applications of DevOps increasing day by day, the future of Azure  DevOps and the engineers proficient in DevOps will be great. However, many things would change and improve with the DevOps. One of the main aspects would be that there would be a lot of containerization of software that applications would run from the cloud itself which could bring a dramatic shift in the world of DevOps! Hence choosing your Azure DevOps Organization plays a vital role. 

 

If you are interested to learn Azure and build a career in Cloud Computing? Then check out our Microsoft Azure Certification Training Course at your near Cities

Microsoft Azure Course Bangalore, Microsoft Azure Course Hyderabad, Microsoft Azure Course Pune, Microsoft Azure Course Delhi, Microsoft Azure Course Chennai, Microsoft Azure Course Newyork, Microsoft Azure Course Washington, Microsoft Azure Course Dallas, Microsoft Azure Course Maryland

These courses are incorporated with Live instructor-led training, Industry Use cases, and hands-on live projects. This training program will make you an expert in Microsoft Azure and help you to achieve your dream job.

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Microsoft Azure Batch Complete Guide – MindMajix

Batch processing refers to the execution of a series of jobs without manual intervention. The execution of programs happens on a series of inputs known as a batch. A batch process performs a series of steps for processing input. These steps are known as jobs.

Azure Batch: 

Azure Batch is a managed service that enables us to run high-performance parallel computing jobs in the cloud. We can run compute-intensive work in a collection of virtual machines. In azure batch, we programmatically define Azure compute resources to execute our jobs. It is an on-demand solution or can be scheduled to run at a specific time.

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Batch service workflows:

  1. First, you need to upload your data files to the Azure storage account. Batch has built-in support for accessing blob storage. While you run the task they will download the data files to compute nodes for processing.
  2. Then you need to upload the applications for running the task. Applications can be scripts or binary files. You can upload them to storage account or package them using application package management and deploy them.
  3. Then you have to create compute nodes. You have to specify the node size and operating system. Batch supports both Windows and Linux operating systems.
  4. Then you have to create a job. Jobs will manage the task and assign them to compute nodes for running and processing your data.
  5. Add a task to the job
  6. Monitor the job and as they finish you can get the output file in your storage account.

Azure Batch Service Resources:

Batch Account:

A batch account is a unique identifier entity for a batch service, which associates all processing in it. A batch account is generally associated with a storage account. 
Batch libraries are used for creating batch accounts in the Azure portal and also can be used for creating batch account programmatically.
The batch account can distribute workloads in the same subscription within different regions. Multiple workloads can be run in a single batch account.

Batch REST API:

Azure batch REST APIs are RESTful APIs that can be accessed via HTTP requests. any service inside azure or outside azure can access Azure Rest APIs.
The batch account is the basic authentication for batch services. The URL of a batch account is like:
https://{account-name}.{region-id}.batch.azure.com

Batch Applications:

Batch applications perform the task on the input file and process them. An application can be a binary file or a script and supporting dependencies.  An application contains one or more application package and specific configuration to run the tasks. Applications can be installed in compute nodes and can be deleted or updated.

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Related article: Azure Applications Insights

Compute Pools:

Compute pool is a collection of computing nodes where the application runs. You can create a pool manually or it can be created automatically by the batch service when work is specified to be done. A batch account can only access the pool it has created and a batch account can have more than one pool.
Azure pool builds on the top of azure compute infrastructure. It supports the scaling of resources and provides health monitoring. 
Every compute node has a unique IP address associated with the pool. When a node leaves a pool the IP is released for future use. 
To create a compute pool we must specify the following attributes:

  1. Compute node operating system and version
  2. Compute node type and target number of nodes
  3. Size of the compute nodes
  4. Scaling policy
  5. 5. Task scheduling policy
  6. Communication status for compute nodes
  7. Start tasks for compute nodes
  8. Application packages
  9. Network configuration

Task virtual Machines:

Task virtual machines are the compute nodes that run the tasks. They are the virtual machines build on the top of azure compute resources. Both Windows and Linux systems are supported. 

  1. Dedicated TVM: Dedicated nodes are expensive and reserved for workloads
  2. Low Priority Nodes: These are in-expensive but can be preempted when Azure has insufficient surplus capacity.
Tasks:

A task runs in a node and is a computational unit for a job. Tasks are assigned to nodes for execution and are queued until one by one processing happens. 

Jobs:

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A job is a collection of tasks. Job manages computation in all nodes in a compute pool.

  • A job specifies the pool where the tasks will be performed. Many pools can be used for a single job or for each job, we can create new pools.
  • Jobs can have priority scheduling and time constraints.

Batch Tutorial 

Here we will use dot net batch library and visual studio to create a sample batch task.
Step 1. Create containers in Azure Blob Storage.
Step 2. Upload task application files and input files to containers.
Step 3. Create a Batch pool.
3a. The pool StartTask downloads the task binary files (TaskApplication) to nodes as they join the pool.
Step 4. Create a Batch job.
Step 5. Add tasks to the job.
5a. The tasks are scheduled to execute on nodes.
5b. Each task downloads its input data from Azure Storage, then begins execution.
Step 6. Monitor tasks.
6a. As tasks are completed, they upload their output data to Azure Storage.
Step 7. Download task output from Storage.

Program.cs //for credentials
// Batch account credentials
private const string BatchAccountName = "";
private const string BatchAccountKey  = "";
private const string BatchAccountUrl  = "";

// Storage account credentials
private const string StorageAccountName = "";
private const string StorageAccountKey  = "";

Step 1: Create a Storage container

Using azure storage client library for dot net create a azure storage account to upload files.
// Construct the Storage account connection string
string storageConnectionString = String.Format(
    "DefaultEndpointsProtocol=https;AccountName={0};AccountKey={1}",
    StorageAccountName,
    StorageAccountKey);

// Retrieve the storage account
CloudStorageAccount storageAccount =
    CloudStorageAccount.Parse(storageConnectionString);

// Create the blob client, for use in obtaining references to
// blob storage containers
CloudBlobClient blobClient = storageAccount.CreateCloudBlobClient();

// Use the blob client to create the containers in Azure Storage if they don\'t
// yet exist
const string appContainerName    = "application";
const string inputContainerName  = "input";
const string outputContainerName = "output";
await CreateContainerIfNotExistAsync(blobClient, appContainerName);
await CreateContainerIfNotExistAsync(blobClient, inputContainerName);
await CreateContainerIfNotExistAsync(blobClient, outputContainerName);
private static async Task CreateContainerIfNotExistAsync(
    CloudBlobClient blobClient,
    string containerName)
{
        CloudBlobContainer container =
            blobClient.GetContainerReference(containerName);

        if (await container.CreateIfNotExistsAsync())
        {
                Console.WriteLine("Container [{0}] created.", containerName);
        }
        else
        {
                Console.WriteLine("Container [{0}] exists, skipping creation.",
                    containerName);
        }
}

Step 2: Upload data files:

// Paths to the executable and its dependencies that will be executed by the tasks
List applicationFilePaths = new List
{
    // The DotNetTutorial project includes a project reference to TaskApplication,
    // allowing us to determine the path of the task application binary dynamically
    typeof(TaskApplication.Program).Assembly.Location,
    "Microsoft.WindowsAzure.Storage.dll"
};

// The collection of data files that are to be processed by the tasks
List inputFilePaths = new List
{
    @"....taskdata1.txt",
    @"....taskdata2.txt",
    @"....taskdata3.txt"
};

// Upload the application and its dependencies to Azure Storage. This is the
// application that will process the data files, and will be executed by each
// of the tasks on the compute nodes.
List applicationFiles = await UploadFilesToContainerAsync(
    blobClient,
    appContainerName,
    applicationFilePaths);

// Upload the data files. This is the data that will be processed by each of
// the tasks that are executed on the compute nodes within the pool.
List inputFiles = await UploadFilesToContainerAsync(
    blobClient,
    inputContainerName,
    inputFilePaths);

Step 3: Create a batch pool:

private static async Task CreatePoolIfNotExistAsync(BatchClient batchClient, string poolId, IList resourceFiles)
{
    CloudPool pool = null;
    try
    {
        Console.WriteLine("Creating pool [{0}]...", poolId);

        // Create the unbound pool. Until we call CloudPool.Commit() or CommitAsync(), no pool is actually created in the
        // Batch service. This CloudPool instance is therefore considered "unbound," and we can modify its properties.
        pool = batchClient.PoolOperations.CreatePool(
            poolId: poolId,
            targetDedicatedComputeNodes: 3,                                             // 3 compute nodes
            virtualMachineSize: "small",                                                // single-vCPU, 1.75 GB memory, 225 GB disk
            cloudServiceConfiguration: new CloudServiceConfiguration(osFamily: "4"));   // Windows Server 2012 R2

        // Create and assign the StartTask that will be executed when compute nodes join the pool.
        // In this case, we copy the StartTask\'s resource files (that will be automatically downloaded
        // to the node by the StartTask) into the shared directory that all tasks will have access to.
        pool.StartTask = new StartTask
        {
            // Specify a command line for the StartTask that copies the task application files to the
            // node\'s shared directory. Every compute node in a Batch pool is configured with a number
            // of pre-defined environment variables that can be referenced by commands or applications
            // run by tasks.

            // Since a successful execution of robocopy can return a non-zero exit code (e.g. 1 when one or
            // more files were successfully copied) we need to manually exit with a 0 for Batch to recognize
            // StartTask execution success.
            CommandLine = "cmd /c (robocopy %AZ_BATCH_TASK_WORKING_DIR% %AZ_BATCH_NODE_SHARED_DIR%) ^& IF %ERRORLEVEL% LEQ 1 exit 0",
            ResourceFiles = resourceFiles,
            WaitForSuccess = true
        };

        await pool.CommitAsync();
    }
    catch (BatchException be)
    {
        // Swallow the specific error code PoolExists since that is expected if the pool already exists
        if (be.RequestInformation?.BatchError != null && be.RequestInformation.BatchError.Code == BatchErrorCodeStrings.PoolExists)
        {
            Console.WriteLine("The pool {0} already existed when we tried to create it", poolId);
        }
        else
        {
            throw; // Any other exception is unexpected
        }
    }
}

Step 4: Create a job:

private static async Task CreatePoolIfNotExistAsync(BatchClient batchClient, string poolId, IList resourceFiles)

{
    CloudPool pool = null;
    try
    {
        Console.WriteLine("Creating pool [{0}]...", poolId);

        // Create the unbound pool. Until we call CloudPool.Commit() or CommitAsync(), no pool is actually created in the
        // Batch service. This CloudPool instance is therefore considered "unbound," and we can modify its properties.
        pool = batchClient.PoolOperations.CreatePool(
            poolId: poolId,
            targetDedicatedComputeNodes: 3,                                             // 3 compute nodes
            virtualMachineSize: "small",                                                // single-vCPU, 1.75 GB memory, 225 GB disk
            cloudServiceConfiguration: new CloudServiceConfiguration(osFamily: "4"));   // Windows Server 2012 R2

        // Create and assign the StartTask that will be executed when compute nodes join the pool.
        // In this case, we copy the StartTask\'s resource files (that will be automatically downloaded
        // to the node by the StartTask) into the shared directory that all tasks will have access to.
        pool.StartTask = new StartTask
        {
            // Specify a command line for the StartTask that copies the task application files to the
            // node\'s shared directory. Every compute node in a Batch pool is configured with a number
            // of pre-defined environment variables that can be referenced by commands or applications
            // run by tasks.

            // Since a successful execution of robocopy can return a non-zero exit code (e.g. 1 when one or
            // more files were successfully copied) we need to manually exit with a 0 for Batch to recognize
            // StartTask execution success.
            CommandLine = "cmd /c (robocopy %AZ_BATCH_TASK_WORKING_DIR% %AZ_BATCH_NODE_SHARED_DIR%) ^& IF %ERRORLEVEL% LEQ 1 exit 0",
            ResourceFiles = resourceFiles,
            WaitForSuccess = true
        };

        await pool.CommitAsync();
    }
    catch (BatchException be)
    {
        // Swallow the specific error code PoolExists since that is expected if the pool already exists
        if (be.RequestInformation?.BatchError != null && be.RequestInformation.BatchError.Code == BatchErrorCodeStrings.PoolExists)
        {
            Console.WriteLine("The pool {0} already existed when we tried to create it", poolId);
        }
        else
        {
            throw; // Any other exception is unexpected
        }
    }
}

Step 5: Add Task to the job:

private static async Task> AddTasksAsync(
    BatchClient batchClient,
    string jobId,
    List inputFiles,
    string outputContainerSasUrl)
{
    Console.WriteLine("Adding {0} tasks to job [{1}]...", inputFiles.Count, jobId);

    // Create a collection to hold the tasks that we\'ll be adding to the job
    List tasks = new List();

    // Create each of the tasks. Because we copied the task application to the
    // node\'s shared directory with the pool\'s StartTask, we can access it via
    // the shared directory on the node that the task runs on.
    foreach (ResourceFile inputFile in inputFiles)
    {
        string taskId = "topNtask" + inputFiles.IndexOf(inputFile);
        string taskCommandLine = String.Format(
            "cmd /c %AZ_BATCH_NODE_SHARED_DIR%TaskApplication.exe {0} 3 "{1}"",
            inputFile.FilePath,
            outputContainerSasUrl);

        CloudTask task = new CloudTask(taskId, taskCommandLine);
        task.ResourceFiles = new List { inputFile };
        tasks.Add(task);
    }

    // Add the tasks as a collection, as opposed to issuing a separate AddTask call
    // for each. Bulk task submission helps to ensure efficient underlying API calls
    // to the Batch service.
    await batchClient.JobOperations.AddTaskAsync(jobId, tasks);

    return tasks;
}

Step 6: Monitor and download the task:

private static async Task DownloadBlobsFromContainerAsync(
    CloudBlobClient blobClient,
    string containerName,
    string directoryPath)
{
        Console.WriteLine("Downloading all files from container [{0}]...", containerName);

        // Retrieve a reference to a previously created container
        CloudBlobContainer container = blobClient.GetContainerReference(containerName);

        // Get a flat listing of all the block blobs in the specified container
        foreach (IListBlobItem item in container.ListBlobs(
                    prefix: null,
                    useFlatBlobListing: true))
        {
                // Retrieve reference to the current blob
                CloudBlob blob = (CloudBlob)item;

                // Save blob contents to a file in the specified folder
                string localOutputFile = Path.Combine(directoryPath, blob.Name);
                await blob.DownloadToFileAsync(localOutputFile, FileMode.Create);
        }

        Console.WriteLine("All files downloaded to {0}", directoryPath);
}

You can additionally perform clean up tasks and deleting the computing pools and jobs.
An azure batch requires programming and platform expertise to perform operations.

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Azure Study Guide

HDInsight Of Microsoft Azure | Features Of Azure HDInsights

Big data described as bulk information. Hadoop is an open-source, Java-based programming framework that supports the processing and storage of Big Data.  A computer cluster is a set of connected computers that can work together as a single system.  Hadoop Clusters are such type of computer clusters that can store, analyse big data which are structured and unstructured. Azure HDInsight deploys these Azure Hadoop clusters in the cloud using the Hortonworks Data Platform (HDP) Hadoop distribution. It also consists of Apache HBase which is a tabular NoSQL database that provides a real-time access to data in HDFS. Apache Storm is a stream analytics platform for processing real-time events like sensors. 

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Features of Azure HDInsights

  • It is mainly used to create, manage and analyse data report statistics on big data. 
  • With the help of virtual machines, you can quickly deploy the system from the Azure portal.
  • You can implement any number of nodes in a cluster.
  • Pay for the service you used.
  • Reuse the cluster to another when a specific job is completed or you can stop using it.
  • The HDInsight service can work with on Microsoft vendors.
  • It is cost-effective to collect and store structured or unstructured data.
  • Extracting undiscovered information from big quantities of unstructured data is easy.
  • Hadoop cluster can be built within minutes.
  • The RESTful API to perform create, read, update, delete (CRUD) operations on text or binary data, like video, audio and images given by the client.
  • The flat network storage system technology offers a high-speed connection between nodes and blob storage system.
  • The master-slave pattern of Insight allows central node or master node to operate and control the cluster centrally. The secondary nodes are integrated into Azure deployments.

The Insights provided by Azure HD are:

  • Disk Usage
  • Utilization of CPU
  • Cluster Load
  • Memory Used
  • Network Used
  • AJAX Calls of a website like no.of views, no.of clicks on particular event etc.

Azure HDinsights Storage Services

It is a general-purpose storage system connected to compute nodes. By storing the data in Azure Storage one has the benefits of data sharing, data achieving, geo-replication and elastic scaling capabilities.  These enable data recovery and redundancy.  The scale-out file system automatically scaled depending upon a number of nodes connected to the cluster.  Every time when a cluster is generated, there is no need to reload the data. Even after the original HDInsight cluster is deleted, you can still use the default storage container.

Azure HDinsights Storage Services

Limitation to Storage Services

  • The Insight Storage service account located in a different location other than the HDInsight cluster location is not supported.
  • Blob storage accounts are not supported.
  • Sharing the default Blob container with multiple HDInsight clusters might corrupt job history kind of cluster-specific information stored in Blob Container.

Live Scenario on Azure HDInsights

Let us consider a healthcare monitoring development and operational cycle.

Scenario on Azure HDInsights

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The above is a health care monitoring process that happens in any hospital. Using Azure HDInsights, you can have time-to-time monitoring on each process, the status of servers and finally depicts faults and errors if occurred. Azure Insight is deployed in the healthcare product. 

After registering the hospital application in the Azure portal and when you start running it, you get the overall performance of healthcare application as given in the below figure.

Performance Metrics

Overall Application Performance Metrics

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Fig: Overall Application Performance Metrics

It shows Browser metrics like page views, page load time, request on each page, each session etc 

Browser metrics

The failures and errors occurred while performing a task in the  application like server exceptions, page faults, data dependency failures etc 

Failures Metrics

Fig: Failures Metrics

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