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 price model and various Azure subscriptions, refer to Subscriptions, licenses, accounts, and tenants for Microsoft’s cloud offerings.
Read MoreAzure 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:
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
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
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
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
Azure Cosmos DB is the first globally distributed, multi-model database service for building world-scale applications. Over the years, it has been providing momentum for Microsoft’s Internet-scale services, which are now available to all Azure developers. The service is designed to allow customers to flexibly and horizontally scale throughput and storage across any number of geographic regions. Read More
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.
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.
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.
As a rule, impersonal information is stored in a public cloud, and the personalized part – in a private cloud. The question thus arises – how to combine both parts to return a single result at a user’s request? Suppose there is a table of customers divided vertically. The depersonalized columns were included in the table located in Windows Azure SQL Database, and columns with sensitive information (e.g., full name) remained in the local SQL Server. Both tables must be linked by the CustomerID key. Because they are located in different databases on different servers, the JOIN statement will not work. As a possible solution, we have considered the scenario, when the linkage was implemented on the local SQL Server. It served as a kind of entry point for the applications, and the cloud-based SQL Server was set up on it as a linked server. In this article, we will consider the case when both, the local and cloud servers, are equal in terms of the application, and the data merging occurs directly in it, i.e. at the business logic level.