In the previous article of this series, Migrate SQL workloads to Microsoft Azure: Databases trip to SQL Server on Azure VM, we went through the methods that can be used for assessing your on-premises SQL Server databases before the migration process then migrating it to a SQL Server instance hosted in a Microsoft Azure VM.
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In the previous articles of this series, we built the bridge that leads us to the best way of migrating our SQL workload to Microsoft Azure, by showing how to create the databases migration strategy that contains a deep study of the current environment and the available options in Azure to replace the on-premises setup, which we tried to make it easier by providing all Azure database services and the workload that fits each service, and finally, we introduced several Microsoft tools and services that can be used to assess the current setup, provide migration guidance steps and migrate the databases to Microsoft Azure services with the minimal downtime.
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In the previous articles of this series we discussed how to draw the initial strategy when planning to migrate the SQL workload to Microsoft Azure, the different database target platforms provided by Microsoft Azure that can be used as a migration target, based on the workload type, and finally, we went through some tools that help in discovering and assessing the on-premises environment by identifying the migration blocking issues and providing guidance steps for the migration process.
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In the previous articles, Migrating SQL workloads to Microsoft Azure: Planning the jump and Migrating SQL workloads to Microsoft Azure: Services Selection, we discussed the steps that you should follow when you start thinking loudly that you need to migrate your SQL workload to Azure, by checking your on-prem site and the available services and features in Azure that meet your workload handling requirements. After that, we went through different database services provided by Azure and which one fits a specific workload, in order to make it easy for you to translate the current workload as an Azure service.
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Using Azure SQL Analytics, you will rediscover the new ways to manage the Azure SQL Database. Microsoft is driving an intelligent platform to provide seamless collaboration for all their end users to business analysts, by building an effective Log Analytics stack and intelligent insight through dashboards.
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In the previous article, Migrating SQL workloads to Microsoft Azure: Planning the jump, we discussed the main points that should be checked and considered while drawing your plan to migrate the SQL workload from the on-premises datacenters to Microsoft Azure. In this article, we will go through the different database services that are provided by Microsoft Azure to help you in selecting the proper service that can serve your SQL workload when migrating it to Microsoft Azure.
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In this article, we will discuss several points that should be considered when planning to migrate the on-premises SQL workload to Microsoft Azure cloud services. This article is the first step in a series of articles that discuss how to perform the SQL and No-SQL workload migration smoothly to the cloud.
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After discussing the basic features of azure machine learning and how to clean the data from Azure Machine learning, let us look at how to perform prediction in Azure Machine Learning. Prediction is one of the important aspects of machine learning as it will help to make strategic decisions.
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In this article, I am going to explain how we can automate the index and statistics maintenance of Azure SQL Database using an Elastic Job Agent.
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In this article, we are going to build a small python application and deploy it to Azure Functions. The development and the deployment will be done using the Visual Studio (VS) Code. As you might be aware, VS Code is one of the most widely used and preferred code editors for programmers. It is a cross-platform tool, which means you can install this tool on any operating system of your choice, starting from Windows, Linux, or Mac OS.
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After discussing the basic features of Azure Machine Learning in my previous article, Introduction to Azure Machine Learning using Azure ML Studio, we will look at techniques of data cleansing in Azure Machine Learning. Data Cleansing or Data Cleaning is an important aspect when it comes to predicting as quality data will improve the quality of data prediction.
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In this article, we will discuss and understand a method to migrate an on-premises SQL Server database to the Azure SQL database.
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In this article, I am going to explain how to get started with Azure Function Apps. In my previous article, An introduction to Serverless Applications, I have mentioned the serverless architecture and the various cloud offerings to develop serverless applications. As we know, in serverless architecture, the users only write the business logic code, and all other worries are taken care of by the cloud provider. This helps businesses to quickly implement solutions and ship it to customers with more quality. Also, another important point about serverless applications is that they are scalable on-demand, which means as a developer, we no longer need to monitor or manually pull up the resources when the executions are more.
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In this article, I am going to describe what are serverless applications and how are these developed. I will also introduce the concepts of serverless computing and how to deploy one using Azure Functions. This article is targeted towards most of the data professionals who want to gain some basic knowledge of serverless applications and the underlying architecture and cloud computing. In recent times, cloud computing has become one of the most important domains for many big organizations and it has thus become essential for all to have some basic information on the same.
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Let us see how Azure ML studio can be used to create machine learning models and how to consume them in this series. As we discussed during the data mining series, we identified the challenges in the predictions in data. In the Azure Machine learning platform, machine learning workflows can be defined in easy scale models in the cloud environment. Today will be looking at how datasets can be uploaded.
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This article is next in the Azure Databricks series, where we will learn how to connect data in Azure Databricks to the Power BI Desktop. We will also talk briefly about visualizations in the Databricks service.
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In my previous article, Getting started with Azure CLI 2.0, I have explained how to get started with the Azure CLI 2.0. I have also explained how to install it on a local machine and demonstrated a few commands. In this article, I’m going to explain how to create a SQL Database using Azure CLI 2.0. Further, in the later part of the article, I’ll also show how to create a backup of the SQL Database using Azure CLI. Creating a SQL Database using the Azure CLI helps us to get things automated and removes the error-prone manual creation of the databases and backups.
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In this article, we are going to understand what Azure CLI is and how to work with it. For the purpose of this article, we will take the version 2.0 to demonstrate the features and functionalities. The Azure CLI, as the name suggests is a cross- platform and open-source command-line tool that we can use to create or manage resources in the Azure Portal. This tool gives us the ability to issue commands right from the windows command prompt and deal with the Azure resources. Since this tool is a cross-platform, that means the users can easily install the utility on Windows, Linux or Mac OS and still work with the same set of commands without making any changes.
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In this article, you will learn about Azure SQL Database and its uses. Then the article splits into two sets of tutorials. The first part will show you how to create a single database in Azure SQL Database. The second tutorial will show you how to build an ASP.NET app in Azure SQL Database.
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In this article, we will discuss several scenario-related interview questions that you may be asked when applying to an Azure Administrator position.
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In this article, we will learn how we can load data into Azure SQL Database from Azure Databricks using Scala and Python notebooks.
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In this article, I am going to explain the step by step process to create an Azure Database for MySQL Server. Azure Database for MySQL is a fully-managed database as a service that uses MySQL community edition. It can manage the mission-critical workload with dynamic scalability. We can use it to develop various applications that leverage open-source tools and cross-platform applications. Let me explain these steps of a deployment process of MySQL Server on Azure.
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We introduced Azure Databricks, how it is created and some of its important components in my previous article here. We will look at how we can work with Azure Blob Storage in Azure Databricks in this article.
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This article serves as a complete guide to Azure Databricks for the beginners. Here, you will walk through the basics of Databricks in Azure, how to create it on the Azure portal and various components & internals related to it.
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In this article, I am going to explain how we can access the data from the Azure Blob Storage using Power BI. To do that, we are going to use the below tools:
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