How to create and configure Microsoft Azure DevOps CI/CD Pipeline Agent

What is CI / CD Pipeline in Azure DevOps?

In the process of learning Azure DevOps, you’ll face numerous challenges, but the most sophisticated of them is setting up the CI Pipeline to work appropriately.

The pivotal point is the Azure Pipelines Agent that establishes and monitors connection with the machine running the DevOps CI Pipeline. Though it is a regular practice, the procedure has its peculiarities. The current tutorial will focus on them and explain how to choose the Azure DevOps CI Pipeline agent and how to configure it correctly.

CodingSight - How to Create and Configure Microsoft Azure DevOps CI/CD Pipeline Agent
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How to Create and Deploy Azure Database for MySQL Server using Azure Portal and Workbench

This article describes the step-by-step deployment process of the Azure Database for MySQL Server.

Open the Azure portal and log in using the appropriate credentials. Note: For demonstration purposes, I got a pay-as-you-go subscription to Microsoft Azure. For more information about the Azure MySQL pricing model and various Azure subscriptions, refer to Subscriptions, licenses, accounts, and tenants for Microsoft’s cloud offerings.

CodingSight - Understanding the Process of an Azure Database Deployment on MySQL
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How to install SQL Server vNext and Azure data studio on Ubuntu 18.04

Azure data studio is a cross-platform database management tool. This tool is used to connect, configure, and administrate SQL Server instances installed locally or in the cloud. This tool can be installed on the following operating systems:

  1. Microsoft Windows
  2. Linux
    • Redgate
    • Debian
    • Ubuntu
  3. macOS

Initially, Microsoft introduced this tool as the “SQL Server operations studio.” Later they renamed it Azure Data Studio. You can get more information about it here.

In this article, I am going to explain the step-by-step installation process of SQL Server 2017 and Azure data studio on Ubuntu. I have divided the article into two parts. Firstly, we will install SQL Server 2017, and later I will explain the process of installing Azure Data Studio. For demonstration purposes, I have created a virtual machine and installed Ubuntu 18.04 on it. You can download Ubuntu here. Read More

Applying SQL Transformations and Handling Missing Values in Azure ML

In this article, we will introduce SQL transformations in action. We will also see how to handle missing values in our dataset.

Consider a scenario of a movie rating dataset containing records of different movies along with the average user ratings associated with each movie. The ratings are in numeric form ranging from 1 to 10 with 1 as the lowest rating and, respectively, 10 as the highest rating (though no movie in the history has achieved 10 rating:). Suppose that we want to convert the numeric ratings into categorical ratings. For instance, we want to replace ratings of 1-3 with the categorical value “poor”, of 4-6 with “average” while ratings of 7-10 will have the value “good”. We can accomplish it with SQL transformation in Azure ML Studio. Read More

Microsoft Graph or Azure Active Directory Graph API: Which is better?

Are you a .NET developer who is stuck in great confusion when it comes to choosing Azure Active Directory or Microsoft Graph?

We all have look at those multiple posts on which you can choose for better web development. In this article, we will be providing some guidance along with a bit of roadmap to clarify things for all of the existing and new developers who want to access directory-based features. Let us begin. Read More

How to Test Azure Web Application Performance through Microsoft Azure and Visual Studio

Before deploying your application into production, doing a performance load test is imperative for measuring future performance and ensure that your application is production-ready.

Testing is essential for every application to make sure that any application works and performs according to the desired requirements. During the application testing process, we can attempt and find out if any imperfections are remaining in the application. There are numerous sorts of testing like functional testing, unit testing, acceptance testing, and integration testing. We compose Functional and UI tests to see whether the application is working as per the requirements. Read More

Getting Started with SQL Server 2017 on Linux in the Azure portal

SQL Server 2017 now is considered as a hybrid database enterprise solution as it expands its market and is ported to other operating system platforms. It also includes mainstream support for Linux machines. The Cloud makes the life of administrator much easier, now it’s no longer daunting task to configure the SQL Server instance. The easiest way to explore SQL Server on Linux is to provision a virtual machine through Microsoft Azure portal – portal.azure.com. The Linux azure virtual machine will come pre-configured with Linux and SQL Server 2017.

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Creating Simple Linear Regression in Azure Machine Learning

In today’s world, it is not enough to simply analyze data, create reports or develop business intelligence projects. To discover the power of data, we have to modify data on machine learning models and to predict future.

In this article, we will discuss one of the simplest methods, a linear regression, that we are going to modify statically in Azure Machine Learning.

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Upload Documents to Azure Data Lake and Export Data using SSIS

 Introduction

Azure is growing every day. Microsoft created Azure, which is a Cloud Computing service released on 2010.

According to Microsoft, 80% of the fortune 500 companies are using Azure. Also, 40% of the Azure Revenue comes from Startups and independent software vendors. 33% of the Azure Virtual Machines are using Linux. Microsoft expects to earn $20 billion in 2018.

That is why companies are migrating part of the data to Azure and sometimes all the data.

Azure Data Lake is a special storage to analyze Big Data in parallel in Azure. It is optimized for analytics. You can store Social network data, emails, documents, sensor information, geographical information and more.

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