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The best way to Compare multiple Ideas: Pair Comparison or Preference Matrix

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  Ideas are the productivity of creativity and implementing ideas is called innovation so before innovation comparing ideas is very critical in the digital transformation journey. In the design thinking process, the Idea or Ideate phase is an important and crucial step. The team develops several ideas for problem-solving or innovation using various available creative processes and principles. When we have a list of various ideas for a solving problem or an innovation then it is very difficult to make a decision to chose one idea that best fit without using a scientific method or practice.  There are various methods for idea comparison and validation are available. The pair comparison method or Preference matrix is one of the simplest methods and best for comparing the ideas and then making decision based on the total score and ranking. If many ideas exist, it is recommended to first select the idea using the pair comparison method in a preference matrix. All ideas are compared in pairs
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What is difference between Azure Cognitive Search and Elastic Search

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  Cognitive search and Elastic search Azure Cognitive Search :-  The Azure Cognitive Search service enables search over different types of content by letting you create and manage search indexes. You can import data from a variety of sources, with AI-powered indexing that can infer and extract searchable content from non-text sources. You decide what data is imported into the index, and set up indexers to pull that data into it, or push JSON formatted documents manually. Azure Cognitive Search also lets you query search indexes. The results contain only your data, which can include text inferred or extracted from images, or new entities and key phrases detection through text analytics. It's a Platform as a Service (PaaS) so Microsoft manages the infrastructure and availability, allowing your organization to benefit without the need to purchase or manage additional hardware resources. Azure Cognitive Search exists to compliment existing technologies and provides a programmable searc
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Transport Layer Security (TLS) best practices with the .NET Framework

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TLS 1.0 is a security protocol first defined in 1999 for establishing encryption channels over computer networks.  Microsoft has supported this protocol since Windows XP/Server 2003.  While no longer the default security protocol in use by modern OSes, TLS 1.0 is still supported for backwards compatibility.  Evolving regulatory requirements as well as new security vulnerabilities in TLS 1.0 provide corporations with the incentive to disable TLS 1.0 entirely.  TLS 1.2 is a standard that provides security improvements over previous versions. TLS 1.2 will eventually be replaced by the newest released standard TLS 1.3 which is faster and has improved security.   To ensure .NET Framework applications remain secure, the TLS version should  not  be hardcoded. .NET Framework applications should use the TLS version the operating system (OS) supports. With TLS 1.2/1.3 change impact the source code uses the following namespace/library/classes. Directly using the  System.Net  APIs (for example,  S
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How Azure Load Balancer Works

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Azure Load Balancer Azure Load Balancer allows us to scale our applications and create highly available services. Azure load balancer allows you to distribute traffic to your backend virtual machines. An Azure load balancer provides high availability for your application. The Azure load balancer is a fully managed service itself. Azure Load Balancer includes a few key components. These components can be configured in your subscription through the Azure portal, Azure CLI, Azure PowerShell, Resource Manager Templates or appropriate alternatives. Following components play and important role in working of Azure Load Balancer. These are Frontend IP configuration The IP address of your Azure Load Balancer. It's the point of contact for clients. These IP addresses can be either: Public IP Address Private IP Address The nature of the IP address determines the type of load balancer created. Private IP address selection creates an internal load balancer. Public IP address selection crea
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How Azure AutoScaling driven up by Azure Monitor Metrics

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  Autoscale is a built-in feature of Cloud Services, Mobile Services, Virtual Machine Scale Sets and Websites that helps applications perform their best when demand changes. Azure Autoscaling driven up by Azure Monitor Metrics data which is collected from Azure Services. Azure Monitor Metrics is a feature of Azure Monitor that collects numeric data from  monitored resources  into a time series database. Metrics are numerical values that are collected at regular intervals and describe some aspect of a system at a particular time. Azure Monitor collects metrics from the following sources. Azure resources Applications Virtual machine agents Custom metrics Data that Azure Monitor Metrics collects is stored in a time-series database that's optimized for analyzing time-stamped data. Each set of metric values is a time series with the following properties: The time that the value was collected. The resource that the value is associated with. A namespace that acts like a c
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Azure based CI/CD Architecture Explained in Just 8 Steps

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Continuous integration and Continuous deployment (CI/CD) architecture using Azure  explained in just simple 8 steps. These are 1. Change application source code. 2. Commit application code and Web Apps web.config. 3. Continuous integration triggers application build and unit tests. 4. Continuous deployment trigger orchestrates deployment of application artifacts with environment-specific parameters. 5. Deployment to Web Apps. 6. Azure Application Insights collects and analyzes health, performance, and usage data. 7.  Review health, performance, and usage information. 8. Update backlog item. Tools/Services use in this process are  Visual Studio Azure Repo Azure Pipeline Azure Web App Service Azure Application Insights Azure Backlog Service or Visual Studio Team Services Backlogs. Ref:- portal.azure.com doc.microsoft.com
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How Azure Application Insights Usage Analysis Help to Improve User Experience, Performance and Business growth

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Application Insights Usages Analysis  Azure Application Insights is a feature of Azure Monitor that provide developer, DevOps Professional and Business extensible Application Performance Management (APM) service. It is used to monitor your live applications. It will automatically detect performance anomalies, and includes powerful analytics tools to help you diagnose issues and to understand what users actually do with your app. It's designed to help you continuously improve performance and usability. It works for apps on a wide variety of platforms including .NET, Node.js, Java, and Python hosted on-premises, hybrid, or any public cloud.  How It Works Application Insights usages analysis helps developer, admin, business owner to find the answer of the following questions. Which features of your web or mobile app are most popular?  Do your users achieve their goals with your app? Do they drop out at particular points, and do they return later? Application Insights provide following
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