• Azure Active Subscription (https://portal.azure.com/)

• Basic Scripting

Introduction to Azure Virtual Machines (VMs)

Azure Virtual Machines (VMs) serve as the backbone of countless applications and services hosted on Microsoft’s cloud platform. Offering unparalleled flexibility and scalability, Azure VMs empower businesses to deploy a wide range of computing solutions, from simple web applications to complex enterprise workloads. These virtualized instances provide on-demand access to computing resources, enabling organizations to swiftly adapt to fluctuating demands without the overhead of managing physical hardware. With Azure VMs, users can choose from a diverse selection of operating systems and configurations, tailoring their environments to meet specific requirements. Whether for development, testing, or production environments, Azure VMs offer a reliable and cost-effective solution for modern cloud computing needs. By automating VM startup and shutdown we can not only optimize the cost but our workloads too.

Importance of Automation in VM Management:

Automating virtual machine (VM) management tasks is crucial for modern cloud environments like Azure. With the ever-growing scale and complexity of cloud deployments, manual management becomes impractical and error-prone.By automate VM startup, shutdown, and scaling, enabling organizations to operate more efficiently, reduce human error, and focus on strategic initiatives. By implementing automation, businesses can ensure consistent performance, optimize resource utilization, and enhance overall productivity.

Benefits of Auto Start and Auto Shutdown Policies:

Auto Start and Auto Shutdown policies offer significant benefits in Azure VM management. Firstly, they enable organizations to optimize costs by automatically starting VMs only when needed, preventing unnecessary usage and expense. Conversely, auto shutdown ensures resources are not left running idle, reducing waste and lowering operational costs. Additionally, these policies enhance reliability and security by ensuring VMs are consistently available during business hours and safely powered off during non-operational times. Overall, Auto Start and Auto Shutdown policies play a pivotal role in maximizing efficiency, minimizing costs, and maintaining a well-orchestrated Azure environment.

Configuring Auto Start for Azure VMs:

Here are the detail steps to configure the auto-start VM in Azure:

• In Azure, First go to Automation Accounts resource and click on create a new resource.

• You have to give your automation account name, resource group and select the region and subscription in which your VM’s are placed and click on create.

• After the resource has been created go to it’s overview page and from there you can find different options in sidebar, look for Runbook, select it and then click on create a new runbook button.

• Give runbook a name, select python workflow as a runbook type. Select its available version and click on create button.

• Now in Automation Account page search for schedules in a sidebar and click on create a new schedule.

• On create a schedule page, you can give your schedule a name and the date in which you want to start schedule and modified it according to your needs.

• Now in runbooks, go to the runbook which you have just created. Click on edit btton at the top, so we can write our modified script in there.

• Write the following script, change the resource group name and vm name according to your environment. After test it and publish it by clicking on publish button.

• Now just click on schedule in runbook page and then click on add a schedule button. And select the schedule which you just had created.

• Now your auto-start runbook is done and it runs on assigned schedule you can check the results in jobs section. In the runbook sidebar to see the output of the runbook process and its done!

Implementing Auto Shutdown for Azure VMs:

• Log in to the Azure Portal  and navigate to the Virtual Machines blade.

• Choose the virtual machine for which you want to set up auto shutdown.

• In the virtual machine’s menu, select “Auto-shutdown” under the Operations section.

• In the Auto-shutdown blade, toggle the “Auto-shutdown” switch to enable the feature. Then, specify the desired shutdown time and time zone. Optionally, you can set a notification to alert users before shutdown.

• Once you’ve configured the auto shutdown settings according to your requirements. Click the “Save” button to apply the changes.

Conclusion:

In conclusion, By automate VM startup and shutdown processes of Azure VMs brings numerous benefits to organizations utilizing cloud infrastructure. By implementing Auto Start and Auto Shutdown policies, businesses can optimize resource utilization, reduce costs, enhance reliability, and improve overall operational efficiency. These policies streamline routine tasks, mitigate human error, and ensure that VMs are available when needed while minimizing waste during idle periods. Through the deployment of automation, organizations can focus their efforts on strategic initiatives rather than mundane maintenance tasks, ultimately driving innovation and competitiveness in the cloud era.

You can checkout our further blogs by going to this link: Blogs

HAPPY LEARNING !

The post Optimizing Azure Workloads: Automate VM Startup and Shutdown appeared first on Impulz Technologies LLC.

Introduction:

In today’s data-driven era, the exponential growth of data has transformed the way organizations operate and make decisions. The sheer volume, velocity, and variety of data generated require sophisticated tools and solutions to extract meaningful insights. Consequently, big data capabilities tools have emerged as the backbone of this transformative process, enabling businesses to harness the power of data for strategic decision-making, innovation, and gaining a competitive edge.

Moreover, these tools play a pivotal role in processing and analyzing vast datasets that traditional databases and analytics tools struggle to manage. The significance of big data tools lies in their ability to uncover patterns, trends, and correlations within massive datasets, providing valuable insights that drive informed decision-making. This not only enhances operational efficiency but also positions organizations to stay ahead in the ever-evolving landscape of data analytics.

Navigating the Big Data Ecosystem: An Overview

As organizations embrace digital transformation, they encounter diverse data types from various sources, including social media, sensors, and IoT devices. Big data tools are essential for ingesting, processing, and extracting actionable intelligence from this diverse and often unstructured data. Moreover, they empower data scientists, analysts, and decision-makers to derive meaningful conclusions, optimize processes, and identify opportunities for growth.

The need for effective big data solutions is underscored by the demands of real-time analytics, predictive modeling, and the imperative to stay agile in an ever-changing business landscape. Whether it’s streamlining operations, improving customer experiences, or gaining a deeper understanding of market trends, big data tools provide the technological foundation for organizations to turn raw data into strategic assets.

In this blog, we’ll delve into a big data capabilities through comparative analysis of prominent big data tools offered by Azure Synapse, Azure Databricks, and Data Factory exploring their features, capabilities, and how they address the evolving challenges of managing and extracting value from massive datasets in today’s dynamic business environment.

Azure Synapse: The Unified Analytics and Data Integration Hub

Azure Synapse Analytics transcends conventional boundaries by merging enterprise data warehousing and Big Data analytics into one coherent service. Furthermore, its unique blend allows for querying data with serverless, on-demand, or provisioned resources at an unparalleled scale. In addition, this synergy between data warehousing and Big Data analytics is designed to provide a unified experience across data ingestion, preparation, management, and serving.

For organizations seeking immediate business intelligence and machine learning needs, Azure Synapse Analytics is a robust choice. Moreover, it bridges the gap between these two worlds, offering a versatile ecosystem for analytics. Whether you require ad-hoc querying, data preparation, or advanced analytics, Synapse has you covered.

Azure Data Bricks: Empowering Data Science and Machine Learning

A fully managed Apache Spark service that can be used for data engineering, data science, and machine learning. It can be used to process large amounts of data quickly and easily. Azure Databricks is a good choice for businesses that need to perform complex data analysis or machine learning tasks.

It empowers you to process massive amounts of data using Apache Spark, a powerful distributed computing engine. Azure Databricks is perfect for big data processing, machine learning, and interactive data exploration. It enables data scientists and engineers to collaborate efficiently and derive valuable insights from complex datasets.

Azure Data Factory: Orchestrating Data Pipelines at Scale

In the realm of big data, orchestrating seamless data workflows is not just a necessity; it’s a strategic imperative. Azure Data Factory stands tall as a robust and versatile solution, empowering organizations to orchestrate data pipelines at scale with unparalleled efficiency and reliability.

Azure Data Factory serves as the conductor, orchestrating the movement and transformation of data across diverse sources and destinations. Its ability to integrate seamlessly with a multitude of data stores, both on-premises and in the cloud, provides the flexibility needed for modern data architectures.

Face-off: Azure Synapse vs. Azure Databricks vs. Data Factory

In the ever-evolving landscape of big data tools, the face-off between Azure Synapse, Azure Databricks, and Data Factory emerges as a critical juncture for organizations navigating the data-driven future. Let’s dissect the strengths, use cases, and unique features of each contender in this heavyweight bout. For this, I ran the same use case three times on these platforms. Each time I got different results based on their performance and cost, which is clearly shown in the graphs below.

Detail Comparison:

Spark Cluster:

Performance Measurement:

Costing Comparison:

Decision Time: Choosing the Right Tool for the Job

• If Unified Analytics and Scalability are Key: Azure Synapse takes the lead.

• For Data Science and Advanced Analytics: Azure Databricks steals the spotlight.

• When Orchestration and Scalable Pipelines Matter: Azure Data Factory emerges as the champion.

Conclusion:

In the subsequent rounds of big data capabilities and their comparative analysis, we’ll delve deeper into each contender, exploring features, use cases, and real-world applications. The big data landscape is vast, exciting, and constantly evolving. Embrace the journey, experiment fearlessly, and stay curious. Your exploration of these tools is not just an exploration of technology; it’s a journey toward transforming data into a strategic asset that propels your organization to new heights of success. Happy exploring!

The post Big Data Capabilities of Azure Synapse, Databricks, and Data Factory appeared first on Impulz Technologies LLC.

Azure DevOps Pipelines is a cloud service that you can use to automatically build, test and deploy your code to (m)any environments. Since Apr 2019 you can use new Azure DevOps tasks for Microsoft Dynamics 365 to upload and deploy your application deployable package to LCS environments. This blog describes how to create build and development server and set up Azure DevOps to build and deploy code for Microsoft Dynamics 365 to LCS environment.

1-Pre-Requisite:

• Before implementing this blog you must have to go through my previous blog How To Setup A Connection Between Azure DevOps & LCS. to successfully create build & development server.

2-Setup Agent Pool:

If you are an organization administrator, you create and manage agent pools from the agent pools tab in admin settings.

1. Sign in to your organization (https://dev.azure.com/{yourorganization}).
2. Choose Azure DevOps, Organization settings.
3. Click on “New agent pool…” button and give it a name.

3-Build Agent Setup

As part of the LCS deployment to a Microsoft-hosted environment, you can configure a Build Agent properties. As a part of the deployment process, you can configure your build agent name and the name of the agent pool that will be this agent owner. The agent pool must exist prior to deployment or the deployment will fail. By default, the “Default” agent pool is used, but here we are using our own agent pool.

• In your LCS environment, go to your Project and select cloud hosted environment setting from the top.
• Here you can select the Add button and after selecting the proper version choose the Dev/Test Environement Topology.
• After that choose the Build/Test environment and in advance setting choose the pool which you were created in past.

• When a Build VM is deployed in Developer topology through LCS, it is pre-configured and ready to start a build. You can change the default configuration at any time from the Visual Studio IDE or the Azure DevOps interface.

3.1-Setup Build Pipeline

• In your Azure DevOps Project Pipeline Section, Select your pipeline and click “Edit”. Default build pipeline will consist of many steps. I will just walk through the most important. As a first step, you give your pipeline a proper name and select the agent pool you created in the previous step.

• Then, you can map source code folders you want to include in the build and cloak the ones you want to exclude. Optionally select Clean options and whether you want to apply Label in your selected Azure repository branch. To distinguish between Labels, you can leverage various built-in variables or create your own. I will talk about variables later.

• These were the basic steps and now you can test your build pipeline. If needed, you can adjust build parameters and finally queue a new build.
• As a result of a successful build, build artifacts are published in your pipeline. Build artifacts are the files produced by your build, e.g. application deployable package. You can download artifacts produced by your build from an instance of the successfully completed build.

4-Developer Agent Setup

To deploy a cloud development environment in your Lifecycle Services (LCS) project:

• Create a connection between an LCS project and your Azure subscription. You’ll need your Azure subscription ID and authorize the use of the subscription.
• Select + under Environments to deploy.
• Select an application and platform version.
• Select an environment topology.
• If you chose a cloud-hosted environment, select which Azure connector you want to use.Then Select Develop Topology.
• Then on next page, simply click the done and your dev machine is deployed, it usually takes 5 to 6 hours approximately for the machine to be in deployed state.
• The user who requests the cloud environment is provisioned as the administrator in that environment.
• User accounts are provisioned on the development VM to allow access to the environment using Remote Desktop, these credentials are accessible on the environment page in LCS.

4.1-Visual Studio Solution and Project Setup

• Login to Azure DevOps then go to your Project > Repos.
• Click the 3 dots next to the Trunk folder to create 2 folders called ‘Dev’ and ‘Main’.
• Create 2 sub-folders called ‘Metadata’ and ‘Projects’ under each of the Dev and Main folders. Your folder structure should look like this:

• Login to your Dev environment.
• Launch File Explorer to create the following folder structure in your C drive.
• Launch Visual Studio in Admin mode.
• Create a new project.

• Type ‘Finance Operations’ in the search field then select Finance Operations and click next.
• Enter your project name, location, and solution name then set your location to “C:\VS\Projects\” folder. Leave the box ‘Place solution and project in the same directory’ unchecked and click create.

• From the main menu, click View > Team Explorer.
• Click the Home icon then Source Control Explorer.
• Click the plug icon at the top to connect your local environment with Azure DevOps Project.
• From the Source Control Explorer, open Workspaces.
• Click ‘add’ to create a workspace:<ComputerName>_Dev.
• Select the Dev workspace then click edit. Map your Source Control Folders to your Local Folders as follows. Click ok when done.

• Switch to your Dev workspace.
• Convert the Dev and Main Folders to Dev and Main Branches:Right click Dev > Branching and Merging > Convert to Branch.Right click Main > Branching and Merging > Convert to Branch.

• In this way, developers work in the Dev branch and when they successfully done their work we simply merge the code from Dev > Main Branch and apply the changes.
• Right-click the Main folder then select ‘Check In Pending Changes’. This will copy the files to the Main folder in DevOps Repos (Main Source Control).

5-Conclusion

• Now you simply have to commit the changes by going to the pending changes section in source control explorer and your code is successfully synced to the Azure DevOps Repo and triggered the build pipeline, In this way you can successfully create build and development server and connect Azure DevOps to automate your package deployment!

Thank you for your time to read this. In part 2, I will discuss how to deploy a build artifact to selected LCS environment.

HAPPY LEARNING!

The post Create Build & Development Server In D365 FO – Part 1 appeared first on Impulz Technologies LLC.

Transferring software deployable packages to the Asset Library is actively enabled by the connection between DevOps and LCS. Here I am describing each step in detail that how can you setup connection between Azure DevOps & LCS so it will be easy for you to deploy your dynamics packages easily in LCS Environment.

1-Workflow:

After you configure an Azure DevOps subscription in Microsoft Dynamics Lifecycle Services (LCS), you can use LCS to deploy developer VMs or build/test VMs. LCS configures a developer VM, which can be linked to an Azure DevOps project. Additionally, LCS sets up a build VM, automatically associating it with an Azure DevOps project. This build VM includes a build agent/controller responsible for compiling modules from the Azure DevOps project and executing automated tests through an external validation endpoint. This workflow includes an LCS deployment of a developer VM and a build/test VM in Azure.

• LCS creates developer and the build/test environments in Azure. To create a build/test environment, LCS must be able to determine where the source code for the Azure DevOps project is.
• Developer works on source code on the developer VM, and the work is synced to the Azure DevOps project.
• The build process synchronizes the code from Azure DevOps onto the build/test VM and produces deployable packages that you can apply to sandbox and production environments. The source code doesn’t flow directly from the development VM to the build/test VM. They are synced through Azure DevOps.

1.1-Create a new Azure DevOps project

• Go to https://www.visualstudio.com/.
• Click Sign in in the upper-right corner.
• Sign in by using an AAD account that is in the tenant that your subscription is linked to. If the browser already has your credentials, you won’t see the sign-in page and should instead click your name in the upper-right corner.
• On the right side of the page, under Accounts, click Create a free account now.
• Specify an account URL, and then click Create Account.
• Name your project, and specify a process template. Your project should now be created.

2-LCS project settings: Set up Azure DevOps:

2.1-Create a personal access token

To connect to an Azure DevOps project, LCS is authenticated by using a personal access token. Follow these steps to create a personal access token in Azure DevOps.

• Go to https://www.visualstudio.com, sign in, and find your Azure DevOps project.
• In the upper-right corner, hold the pointer over your name, and then, on the menu that appears, select Security.
• Select Add to create a new personal access token.
• Enter a name for the token, and then specify how long the token should last.
• Select Create Token.
• Copy the token to your clipboard.

2.2-Configure your LCS project to connect to Azure DevOps

• In this step we see how to setup connection between Azure DevOps & LCS
• In your LCS project, select the Project settings tile.
• Select Azure DevOps, and then select Setup Azure DevOps. This configuration is required by many LCS tools. If you’ve already configured LCS to connect to your Azure DevOps project, you can either skip this procedure or select Change to change the existing configuration.
• Enter the root URL for your Azure DevOps account, and the personal access token that you created earlier, and then select Continue.
• Select your Azure DevOps project.
• LCS requires entering the Azure DevOps root URL in the legacy format. The legacy format is and .
• Specify the mapping between LCS/BPM items and the associated Azure DevOps work item types.
• Select Continue, review your changes, and then select Save.

3-Set up Azure Service Connection in Azure DevOps:

3.1-Create an App Registration

• Now create a new “App Registration” under Azure Active Directory with a name like “D365 – DevOps to LCS”
• Under “Supported account Types” please select “Accounts in this organizational directory only (“Your company” only – Single tenant) 

• Now click “Registrer”
• Create an app registration and make a note of the Application (Client) Id.

• Proceed to the ‘Authentication’ section within the app registration.
• Choose ‘Yes’ for the option to ‘Treat the application as a public client.’
• You can adjust this setting before moving on to ‘Add a platform’ if necessary..

• When you now “Add a platform” select the “Mobile and Desktop application”
• Set a checkmark by the native URL … and click on ‘Configure’
• Now ‘Save’ your “App Registration”
• Now open the “API permissions” and “Add a permission”

• Click on the tab ‘APIs my organization uses” and search for “Dynamics”, select “Dynamics Lifecycle services”
• Now select “Delegated permissions” make sure the checkmark is set by permissions at “user_impersonation” and click “Add permissions”
• Now click on “Grant admin consent for <Your Company>” and select ‘Yes’

3.2-Create the Service Connection in DevOps

To create a service connection in DevOps it is required that you have installed the extension “Dynamics 365 Finances and Operations tools” (it’s free) into the DevOps, you can find it here:

https://marketplace.visualstudio.com/items?itemName=Dyn365FinOps.dynamics365-finops-tools

Once it is installed you can verify it by going to the Organization settings and in extension column like this

Now open you Project settings in your DevOps and select ‘Service Connections*’

Click ‘ New service connection’ and select “Dynamics Lifecycle Services” and click Next.

and now fill out the Username , Password and the Application Client Id. Click Save and you are done.

4-Set up Azure Connecter in LCS:

4.1-Create a new role in Azure Portal

• On your Azure Portal, go to your Azure Subscription.
• Go to IAM Settings tab, and then click create a new role button.
• Give new role Contributor access.
• For Assign Access to Select Azure Ad user, service principals.
• Then select Dynamics Deployment Services [wsfed-enabled].

• You can go to LCS and then on Project Settings column select Azure Connector.
• Here you click Add and then simply fill the naming field of your own choice, for Subscription ID you have to add your Active Subscription ID where you had created a role and then select Yes to configure to use ARM.

• In the Authorization Page, download the required certification file for authenticating LCS with your Azure Environment.
• Navigate to the ‘Management Certificates’ tab in your Azure Subscription and upload the certificate acquired from LCS, which will serve as the authentication credential. After the successful upload of the certificate, go back to your LCS environment.
• Click ‘Next,’ select your preferred region, and finally, click ‘Connect’ to complete the setup.

In this way we setup connection between Azure DevOps & LCS Environment and setup the pre-requisite of our Build and Development Environment in Azure DevOps.

The post How To Setup A Connection Between Azure DevOps & LCS appeared first on Impulz Technologies LLC.

• Check number.
• Beneficiary name.
• Check issue date.
• Check amount.

This seems to be a fairly straight forward process? Actually this is not a straight forward process as we think. Every bank has its own unique file formats requiring data in a specific file format. Printing a check is a widely used feature in Microsoft Dynamics 365 Finance and Supply Chain. Microsoft has provided multiple ways to extract the positive pay file out of Dynamics and it is always improving since AX 2012. Youcan read here about how to configure the positive pay file export in Dynamics 365. The only downside which I found is that it still requires some knowledge of the XML (Extensible Markup Language) and XSLT (Extensible Style Sheet Language Transformation) which business users lack.

Identifying this as a product gap, Impulz Technologies has recently upgraded its flagship Microsoft Dynamics 365 add-on for the US market, Impulz SafePay. With Impulz SafePay, an end-user can configure the entire bank file format using the familiar and user friendly screens. Impulz SafePay, also ships with pre-configured formats which further reduces the configuration time required.

We will be publishing the further details about our Impulz SafePay tool in coming weeks. If you like to schedule a product demo or just want to talk more about services then please free to contact us.

The post Check Writing in the Corporate USA appeared first on Impulz Technologies LLC.