Overview of porting from .NET Framework to .NET

This article provides an overview of what you should consider when porting your code from .NET Framework to .NET (formerly named .NET Core). Porting to .NET from .NET Framework for many projects is relatively straightforward. The complexity of your projects dictates how much work you’ll do after the initial migration of the project files.

Projects where the app-model is available in .NET (such as libraries, console apps, and desktop apps) usually require little change. Projects that require a new app model, such as moving to ASP.NET Core from ASP.NET, require more work. Many patterns from the old app model have equivalents that can be used during the conversion.

Unavailable technologies

There are a few technologies in .NET Framework that don’t exist in .NET:

  • Application domainsCreating additional application domains isn’t supported. For code isolation, use separate processes or containers as an alternative.
  • RemotingRemoting is used for communicating across application domains, which are no longer supported. For communication across processes, consider inter-process communication (IPC) mechanisms as an alternative to remoting, such as the System.IO.Pipes class or the MemoryMappedFile class.
  • Code access security (CAS)CAS was a sandboxing technique supported by .NET Framework but deprecated in .NET Framework 4.0. It was replaced by Security Transparency and it’s not supported in .NET. Instead, use security boundaries provided by the operating system, such as virtualization, containers, or user accounts.
  • Security transparencySimilar to CAS, this sandboxing technique is no longer recommended for .NET Framework applications and it’s not supported in .NET. Instead, use security boundaries provided by the operating system, such as virtualization, containers, or user accounts.
  • System.EnterpriseServicesSystem.EnterpriseServices (COM+) isn’t supported in .NET.
  • Windows Workflow Foundation (WF) and Windows Communication Foundation (WCF)WF and WCF aren’t supported in .NET 5+ (including .NET Core). For alternatives, see CoreWF and CoreWCF.

For more information about these unsupported technologies, see .NET Framework technologies unavailable on .NET Core and .NET 5+.

Windows desktop technologies

Many applications created for .NET Framework use a desktop technology such as Windows Forms or Windows Presentation Foundation (WPF). Both Windows Forms and WPF have been ported to .NET, but these remain Windows-only technologies.

Consider the following dependencies before you migrate a Windows Forms or WPF application:

  1. Project files for .NET use a different format than .NET Framework.
  2. Your project may use an API that isn’t available in .NET.
  3. 3rd-party controls and libraries may not have been ported to .NET and remain only available to .NET Framework.
  4. Your project uses a technology that is no longer available in .NET.

.NET uses the open-source versions of Windows Forms and WPF and includes enhancements over .NET Framework.

For tutorials on migrating your desktop application to .NET 5, see one of the following articles:

Windows-specific APIs

Applications can still P/Invoke native libraries on platforms supported by .NET. This technology isn’t limited to Windows. However, if the library you’re referencing is Windows-specific, such as a user32.dll or kernel32.dll, then the code only works on Windows. For each platform you want your app to run on, you’ll have to either find platform-specific versions, or make your code generic enough to run on all platforms.

When porting an application from .NET Framework to .NET, your application probably used a library provided distributed with the .NET Framework. Many APIs that were available in .NET Framework weren’t ported to .NET because they relied on Windows-specific technology, such as the Windows Registry or the GDI+ drawing model.

The Windows Compatibility Pack provides a large portion of the .NET Framework API surface to .NET and is provided via the Microsoft.Windows.Compatibility NuGet package.

For more information, see Use the Windows Compatibility Pack to port code to .NET.

.NET Framework compatibility mode

The .NET Framework compatibility mode was introduced in .NET Standard 2.0. This compatibility mode allows .NET Standard and .NET 5+ (and .NET Core 3.1) projects to reference .NET Framework libraries on Windows-only. Referencing .NET Framework libraries doesn’t work for all projects, such as if the library uses Windows Presentation Foundation (WPF) APIs, but it does unblock many porting scenarios. For more information, see the Analyze your dependencies to port code from .NET Framework to .NET.


.NET (formerly known as .NET Core) is designed to be cross-platform. If your code doesn’t depend on Windows-specific technologies, it may run on other platforms such as macOS, Linux, and Android. This includes project types like:

  • Libraries
  • Console-based tools
  • Automation
  • ASP.NET sites

.NET Framework is a Windows-only component. When your code uses Windows-specific technologies or APIs, such as Windows Forms and Windows Presentation Foundation (WPF), the code can still run on .NET but it won’t run on other operating systems.

It’s possible that your library or console-based application can be used cross-platform without changing much. When porting to .NET, you may want to take this into consideration and test your application on other platforms.

The future of .NET Standard

.NET Standard is a formal specification of .NET APIs that are available on multiple .NET implementations. The motivation behind .NET Standard was to establish greater uniformity in the .NET ecosystem. Starting with .NET 5, a different approach to establishing uniformity has been adopted, and this new approach eliminates the need for .NET Standard in many scenarios. For more information, see .NET 5 and .NET Standard.

.NET Standard 2.0 was the last version to support .NET Framework.

Tools to assist porting

Instead of manually porting an application from .NET Framework to .NET, you can use different tools to help automate some aspects of the migration. Porting a complex project is, in itself, a complex process. These tools may help in that journey.

Even if you use a tool to help port your application, you should review the Considerations when porting section in this article.

.NET Upgrade Assistant

The .NET Upgrade Assistant is a command-line tool that can be run on different kinds of .NET Framework apps. It’s designed to assist with upgrading .NET Framework apps to .NET 5. After running the tool, in most cases the app will require more effort to complete the migration. The tool includes the installation of analyzers that can assist with completing the migration. This tool works on the following types of .NET Framework applications:

  • Windows Forms
  • WPF
  • Console
  • Class libraries

This tool uses the other tools listed in this article and guides the migration process. For more information about the tool, see Overview of the .NET Upgrade Assistant.


The try-convert tool is a .NET global tool that can convert a project or entire solution to the .NET SDK, including moving desktop apps to .NET 5. However, this tool isn’t recommended if your project has a complicated build process such as custom tasks, targets, or imports.

For more information, see the try-convert GitHub repository.

.NET Portability Analyzer

The .NET Portability Analyzer is a tool that analyzes assemblies and provides a detailed report on .NET APIs that are missing for the applications or libraries to be portable on your specified targeted .NET platforms.

To use the .NET Portability Analyzer in Visual Studio, install the extension from the marketplace.

For more information, see The .NET Portability Analyzer.

Platform compatibility analyzer

The Platform compatibility analyzer analyzes whether or not you’re using an API that will throw a PlatformNotSupportedException at run time. Although this isn’t common if you’re moving from .NET Framework 4.7.2 or higher, it’s good to check. For more information about APIs that throw exceptions on .NET, see APIs that always throw exceptions on .NET Core.

For more information, see Platform compatibility analyzer.

Considerations when porting

When porting your application to .NET, consider the following suggestions in order.

✔️ CONSIDER using the .NET Upgrade Assistant to migrate your projects. Even though this tool is in preview, it automates most of the manual steps detailed in this article and gives you a great starting point for continuing your migration path.

✔️ CONSIDER examining your dependencies first. Your dependencies must target .NET 5, .NET Standard, or .NET Core.

✔️ DO migrate from a NuGet packages.config file to PackageReference settings in the project file. Use Visual Studio to convert the package.config file.

✔️ CONSIDER upgrading to the latest project file format even if you can’t yet port your app. .NET Framework projects use an outdated project format. Even though the latest project format, known as SDK-style projects, was created for .NET Core and beyond, they work with .NET Framework. Having your project file in the latest format gives you a good basis for porting your app in the future.

✔️ DO retarget your .NET Framework project to at least .NET Framework 4.7.2. This ensures the availability of the latest API alternatives for cases where .NET Standard doesn’t support existing APIs.

✔️ CONSIDER targeting .NET 5 instead of .NET Core 3.1. While .NET Core 3.1 is under long-term support (LTS), .NET 5 is the latest and .NET 6 will be LTS when released.

✔️ DO target .NET 5 for Windows Forms and WPF projects. .NET 5 contains many improvements for Desktop apps.

✔️ CONSIDER targeting .NET Standard 2.0 if you’re migrating a library that may also be used with .NET Framework projects. You can also multitarget your library, targeting both .NET Framework and .NET Standard.

✔️ DO add reference to the Microsoft.Windows.Compatibility NuGet package if, after migrating, you get errors of missing APIs. A large portion of the .NET Framework API surface is available to .NET via the NuGet package.

Source: https://docs.microsoft.com/en-us/dotnet/core/porting/

Microsoft Power Platform – The most powerful and emerging platform for business operations across the cloud

Microsoft has launched Microsoft Power Platform as a gamut of products that assist businesses to specialize their business operations, automate business processes, and deliver business data and analytics and more. These products comprise of a simple and easy graphic user interface that is designed to perform across most businesses and organizations.

The Power Platform comprises of most Microsoft skills which reduce the need of IT support within the organization. Hence, it makes sense to learn all about the Microsoft Power Platform and how to use to deliver a far more superior business data and analytics

Why should organizations use the Microsoft Power Platform?

Across the world, cloud services are being increasingly preferred by most organizations and businesses to store data and also access data as per requirement. Microsoft Power Platform is designed to empower the employees to maximize the business data and intelligence available across the cloud platform for further growth and achieving business targets. It can be stated that Microsoft Power Platform offers better sustainability and helps organization realizing their real competencies.

Microsoft Power Platform offers the right platform with and efficient support system to these organizations enabling them to work seamlessly with the data and deliver business intelligence for further growth. The users of this platform will be able to analyze, act and automate the data across the platform ensuring a comprehensive experience.

Tools available across the Microsoft Power Platform –

Power BI – This self-service tool can be used for analyzing business data that is derived from various sources. Power BI is a core part of the power platform which the users find helpful in directing them towards business growth.

Power Apps – This app makes it possible to build mobile apps which can be used for internal operations within the organization. Power Apps comes with a drag and drop interface through which users can build mobile applications for internal use.

Power Automate – Aptly titled, Power Automate assists in automating the workflow within the organization. This implies eliminating or reducing the need for manual business operations. This app does not require coding when automating workflows within the business. This app was earlier known as Microsoft Flow.

Power Virtual Agents – Users can use Power Virtual Agents to develop chatbots which are immensely useful for setting up communication channels within external parties especially customers. A certified Microsoft Power Platform expert can develop and deliver chatbots that too, without coding. Summarizing the above, Microsoft Power Platform empowers users with superior business data analytics, developing mobile apps and chatbots, and automation of workflows which implies a great deal of convenience and ease in business operations.

Amidst the current The Microsoft Power Platform extends its support within today’s rapidly evolving business scenarios, as there is always an impending need to have access to real-time solutions without any time lag for capitalizing the business.

With Microsoft Power Platform offers the following

One window to handle all business processes Imagine the convenience if all the required business data can be accessed without delay in time. With the cloud infrastructure and data warehouse in place, Microsoft Power Platform enables to leverage the data through high end business reports and graphical representation too. An existing Microsoft Power Platform user is quite familiar with automated business processes, developing chatbots and mobile apps that smoothen both internal as well as external business operations. But with the offering of a single window or platform, the user enjoys the added advantage of quick access to business data, its analysis and much more. At the same time, the user does not need to visit different platforms, storage units to gain access to data and use it, every possible data from a single or a varied source is available through the single window.

Intuitive User Interface including its Adoption

Yes, that is what the Microsoft Power Platform is meant to be. With an integrated IT setup that uses Microsoft Power Platform, the need for having an expanded IT resources reduces to a great extent. Certified Microsoft Power Platform resources could prove their worth and deliver as per the expectations of the organization. Imagine building mobile apps and chatbots without coding but ensuring smooth business operations for enhanced business performance on one side while giving the required bandwidth to the operations team to devote their core resources on accomplishing business strategies. It is definitely a win-win situation for the entire organization.

It’s Microsoft – Reliable and Trustworthy Organizations have relied on Microsoft products and services for decades now. As with every other Microsoft product or service, Microsoft Power Platform comes with a seal of trust and reliance. Every added app or tool is time-tried and tested and delivers as per its expectations. Be it Power BI for analytical business data, automation of workflows through Power Automate or building chatbots using Virtual Agents or mobile apps through PowerApps, the Microsoft Power Platform is a one-stop comprehensive solution for every future-oriented business who using the cloud services.

It is time to go ahead and purchase the Microsoft Power Platform and once it is up, running and operational, organizations can –

 • Simplify business tasks

 • Faster execution of business tasks and operations

• Independence from using third party tools and processes

Sooner or later, it is certain that Microsoft Power Platform is going to be recognized as the key requisite for all organizations which have migrated to the cloud platform. Apart from being an intuitive interface, it offers solutions to business data and intelligence, comprehensive representation of business reports along with automating business processes and developing chatbots and mobile apps all directing towards enhancing customer and operations experience relates very much to its intuitive development by Microsoft. 

2021 will be the year of MLOps

We need to start treating AI projects like wider software development by applying a machine learning operations (MLOps) model.

January 2021 is the customary time to make predictions on what the year holds in store. Working in partnership with companies across multiple industries that are looking to develop data science and AI skills in their workforce, I have a good vantage point on the trends that are developing across the realm of technology. In addition, I have published recent research with colleagues at Cambridge University about the challenges that face organizations with deploying machine learning. From this perspective, there is a clear picture forming that 2021 will be a turning point within leading businesses for making a priority of operationalizing AI. In fact, the second half of 2020 has seen a new crop of tools, platforms and startups receiving investment to provide solutions to this difficult problem.

This prediction may seem surprising to some readers, who might rightly point to enterprises that have already launched AI projects. Indeed, there have been dozens of AI proof of concept (PoC) projects in the headlines over the past few years. International breweries using AI to improve operations, energy conglomerates creating a “digital power plant”, car manufacturers trying to predict when a vehicle will need servicing.

However, the key distinction here is whether these projects ever move past the PoC phase into full operation. Moreover, even if they do, whether these AI deployments actually deliver any value for the business beyond publicity.

Gartner predicted that 80 percent of AI projects in 2020 remain what they call “alchemy, run by wizards whose talents will not scale in the organization.” Others are even more pessimistic, with Algorithima’s 2020 State of Enterprise Machine Learning report estimating that only 8 percent of enterprises have sophisticated models in production.

There is nothing intrinsically wrong with pursuing PoC projects. In fact, I would encourage all organizations to start with low-hanging fruits and look at how they can “hack” their processes as a way of enabling experimentation, innovation and unlocking commercial advantage. However, it is beyond time that some of these pursuits work towards a destination of operationalization and scalable value.

More than anything else, if enterprises continue with the approach of getting excited about running experiments and stopping there, there is a risk that AI and ML will be seen as nothing more than a financial hole. The myriad opportunities for ML to have a material impact on a company’s bottom line may never be realized if the funding runs out before we get to the operational stage. As an industry, then, we need to move past geeky PoCs and start actual deployments that will make businesses money.

The evolution of MLOps

Thankfully, there are a lot of lessons that can be learnt from the Software Engineering field and DevOps principles. The catalyst that may finally move the majority of AI projects beyond “alchemy” and into robust engineering in 2021 is called MLOps. This combination of “machine learning” and “operations” is, in the simplest terms, the practice for collaboration and communication between data scientists, developers and platform engineers to improve the production lifecycle of ML projects.

It is not a new idea. In fact, the term is appropriated from DevOps, a practice that has existed for two decades. Practicing MLOps means to follow standardisation and processes to automate and monitor all steps of the ML deployment workflow, including data and infrastructure management, model learning, testing, integration, releasing, deployment and security. Ultimately, MLOps expedites and removes the pain from embedding ML into scalable systems.

While 2021 is not the year the idea MLOps was conceived, there is strong evidence to suggest that it will be the year we see enterprises formalize an MLOps strategy and put it into practice.

This is partly driven from demand within organizations to reduce production cycles because of the high cost that has yet to be converted into ROI. According to the Algorithmia report, 22 percent of companies have had ML models in production for 1-2 years. Naturally, these organizations will be applying pressure to see results sooner rather than later and MLOps is a solution for shortening the time it takes to put a model into full production.

Tools and skills

Clearer evidence of the acceleration of MLOps as a practice can be seen in the investment into MLOps products. Like software development, MLOps requires an ecosystem of tools and frameworks that industrialize the process of creating ML models and create an environment for developers and operational professionals to collaborate. The availability of solutions is one of the fundamentals needed for MLOps to be put into practice.

Companies such as H2O and DataRobot have led the way in the field of AutoML tools but there has now been an explosion of startups being funded in this space. A new report from Cognilytica predicts exponential growth of the market, to the tune of $126.1 billion by 2025. This represents a 33.73 percent compound annual growth rate and demonstrates the recognition in the industry that new tools and platforms are required for AI deployments to be successful.

Once the tools are in place, the last piece of the puzzle in creating true MLOps is skills. It goes without saying that to build a consistent approach to ML will require a skills pool beyond what Gartner describes as the AI “wizards”. The talent pool has to be dramatically expanded to ensure that there are enough skilled professionals on both the developmental and operational sides of MLOps to bring the tools and processes together. Through our own apprenticeships and courses, we have seen a significant increase in demand, which once again suggests that enterprises are beginning to approach MLOps in earnest.

The implementation of MLOps and closer collaboration of software developers and AI practitioners will bring a maturity to the market in 2021. This will mean more processes and systems that enable the scaling and acceleration of machine learning capabilities. Hopefully, this will make 2021 the year that enterprises begin to reap the benefits of AI deployment through efficiencies and savings, leading to more investment in innovation.

Source: https://www.itproportal.com/features/2021-will-be-the-year-of-mlops/

Learn about Azure Machine Learning and a Data Science Solution on Azure

A shout out to data scientists, data engineers & data analysts who wish to move ahead in their career. In this blog, we are going to focus on DP-100 Certification and how it fast tracks the career path of these profiles.

Microsoft’s DP-100 Certification teaches how to operate machine learning solutions across the cloud using the Azure Machine Learning.  Additionally, you can leverage the existing knowledge of Python and machine learning in Microsoft Azure. The key tasks include learning all about managing data ingestion and preparation, model training and deployment, and machine learning solution monitoring

Synergetics Learning offers to train people in appearing for this certification. Our Microsoft certified trainers conduct extensive and well-defined training spread over a 3-day training period. This certification is for passionate Azure Data Scientists who have working knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

Upon certification, they will be able to deliver the following:

  • Create cloud resources in Microsoft Azure.
  • Use Python to explore and visualize data.
  • Learn to use like Scikit-Learn, PyTorch, and TensorFlow and other common frameworks for training and validating machine learning models.

The interested candidates can attend our free online training sessions exploring Microsoft cloud concepts and creating machine learning models before enrolling for DP-100 course. Candidates who have completed the Microsoft Azure AI Fundamentals first will be able to grasp the fundamentals in a better way.

Synergetics Training – Modules of DP-100 certification in detail

Module 1: Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Module 2: No-Code Machine Learning with Designer

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code and use them to train machine learning models.

Module 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Module 5: Compute Contexts

One of the key benefits of the cloud is the ability to leverage compute resources on demand and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Module 6: Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.

Module 7: Deploying and Consuming Models

Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Module 8: Training Optimal Models

By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Module 9: Interpreting Models

Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It’s increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model’s behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.

Module 10: Monitoring Models

After a model has been deployed, it’s important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

All about DP-100 examination

The DP-100 exam could include approximately 40 – 60 questions with case studies, or in multiple choice questions or completion of code or sequential arrangement of components, and so on within a time frame of 180 minutes.

Synergetics Consulting offers to guide Data Scientists, Data Engineers & Data Analysts and other eligible candidates in reviewing the objectives of the DP-100 examination with regards to the topics and the likely questions in the examination. We offer instructor-led training too, to interested candidates.

The next important element in your preparations for the DP-100 exam refers directly to the exam objectives or the blueprint. The exam blueprint provides a clear idea of the different topics that would serve as the basis for questions in the DP-100 exam. At the same time, we offer practice tests with learning resources and materials for the best outcomes of the participating candidates.

Data Science is very much in demand and hence if you are interested in this profile, DP-100 certification is the most suitable exam to surge ahead. Also, in the coming time, data analytics is very important as huge amount of data is collected through the use of increasing number of smart connected devices that are being used across the world. Now you can update your skills with today’s highly-demanded Information Technologies but all you need is the right guidance.

“Synergetics Learning” is known for intuitive training, each training is provided by experienced in Microsoft Certified Trainers with lots of hands-on experience. If you wish to enroll for DP-100 or any other related certification, do connect with Synergetics at info@synergetics-india.com or call us on +91 8291362058. You can also visit our website for more details.

DA-100 – A certification that offers exceptional skills in Data Analytics for Business Intelligence

Wish to specialize in business intelligence or enhance your profile of a business analyst while you work your way with business data? If your answer is in the affirmative and then let us explore the best possible avenue that offers the same.

Microsoft’s Exam DA-100: Analyzing Data with Microsoft Power BI is the most preferred certification to acquire to become an expert Data Analyst. This Power BI certification will make you capable to evolve strategies for further business growth by efficient management and analysis of business data. They empower you to develop, organize and use data structures and their sources more efficiently.

The role of a Business Intelligence Architects is entwined with key understanding of business requirements and data skills in order to analyze data for business competencies along with report writing.

Power BI – Offerings and Expertise

Microsoft Power BI is based on Microsoft Excel. It refers to a gamut of cloud-based apps and services that use a simple user interface to capture, process and analyze data from multiple sources as required by its business entity or organization.

Moreover, Power BI goes one step ahead to reveal observations and analyzed business data through graphs and business charts for better understanding and impact. The data analysts can share actionable charts and graphs of the business trends for further evaluation.

Power BI works on data and can be used across varied divisions in the organization viz. marketing, sales, finance, information technology, operations and human resources along with the others.

Key services of Power BI are –

  • Power Query for combining and enhancing data collected from different sources.
  • Power Pivot a vital tool for developing data models from the data base
  • Power View is a powerful tool that helps you to create maps, charts and visuals in an interactive mode.
  • Power Map empowers you to make 3D model with its 3D features.

Making Business Reports using Microsoft POWER BI

You need to personally explore the power of POWER BI in generating reports. Power BI collates and bind data from varied sources to process, sort and filter relevant information for better grasping and understanding. Additionally, this data can be used to generate reports and graphics for presentation and decision making.

Power BI is useful for predicting future trends. Data processing through Power BI enables to see the past performance along with visualization of the forthcoming times. It is fortified with machine learning algorithms that draw attention on patterns in the data and work on those to envision similar patterns in the future.  It is capable of highlighting ‘What if’ scenarios’ based on analytical data which can be quite substantial in most cases.

Role of Business Intelligence Architects in conjunction with Power BI

As mentioned earlier, armed with a DA -100 certification, the Business Intelligence Architect is capable of delivering well researched and analyzed business intelligence data as required by the decision-making authorities. As a Business Intelligence Architect, you will be able to:

  • Seek large volumes of data and process it through Power BI at great speeds.
  • Analyze the business data using machine learning features much faster than other colleagues.
  • Present intelligent and comprehensive predictions based on business data.
  • Present graphic and visual representation of data for better understanding.
  • Enjoy data security and
  • Work on an intuitive and user-friendly platform

DA 100 – the Best Power BI Certification Course

The DA 100 course is just right for those who wish to work with business data. They can gain expertise in the profile of a Business Data Analyst while earn handsome salary as well.

DA 100 is a mid-level certification offered by Microsoft for people who wish to gain meaningful entry in the IT sector. The DA 100 certification will help you use the Power BI software, model the data through the software and also enable you to visualize data through it.

The skills and their weightage judged in the DA-100 certification are as follows:

  • Prepare the data (20-25%)
  • Model the data (25-30%)
  • Visualize the data (20-25%)
  • Analyze the data (10-15%)
  • Deploy and maintain deliverables (10-15%)

Indeed, this is definitely an interesting and a challenging profile to explore if you wish to have a solid career in the information sector.  Candidates with working knowledge of Microsoft Azure or those who have had experience in working as Azure Developer and across the cloud platform.

To reinforce your decision on going forward with the DA-100 certification, we share some useful pointers.

  • DA-100 is a medium-level difficulty certification. You will be aware of various concepts and their application across multiple segments and channels.
  • The passing score of the DA-100 certification is 700 marks out of the total 40 questions which carry a sum total of 1000 marks.
  • This certification is suitable for those love to work on data and its analytics. They should know how to connect to data sources and how to make the best use of data. Also, most importantly what matters the most is the implementation and application of data for business intelligence.

Why Synergetics for training on DA-100 certification?

Synergetics is one of the oldest and leading training partners of Microsoft Certifications. Synergetics Learning offers a 4-day intensive training for the DA-100 certification.

This training focuses on the following parameters within the POWER BI framework:

  • Various Methods and best practices that are on par with modeling, visualizing, and analyzing data.
  • How to access and process data from a range of data sources that includes relational and non-relational data.
  • Implementation of proper security standards and policies across the Power BI spectrum including datasets and groups.
  • Management and deployment of reports and dashboards for sharing and content distribution.
  • How to build paginated reports within the Power BI service and publish them to a workspace for inclusion within Power BI.

At Synergetics, our Microsoft Certified Trainers deliver exceptional training through interactive learning sessions and ample practice through sample test for exam preparation. Also, available is our Hands on Lab which the students can take advantage of before appearing for their certification exam.

Do approach Synergetics Learning, a Microsoft Training Partner if you are interested in DA-100 certification and wish to make considerable progress in your career path. For more details, contact us at info@synergetics-india.com and +91 8291362058. You can also visit our website for more details.

The future of DevOps: Predictions for 2021

Now that DevOps has entered its second decade, the focus has expanded beyond product delivery. It’s no longer just about dev and ops, but about removing the constraints between the business and its customers, with a focus on delivering not just new features and products, but also value. So what comes next as DevOps evolves?

Here are their predictions.

Culture and leadership

Business leaders will increasingly value DevOps, showing that the work of the DevOps enterprise community matters to the people who matter.

One of the most amazing dynamics within the DevOps enterprise community is seeing business leaders co-presenting success stories with their technology leadership counterparts. For example, Ken Kennedy (executive vice president and president for Technology and Product at CSG) and Kimberly Johnson (chief operating officer at Fannie Mae) described the achievements of their technology leadership counterparts and why it was important to them. I expect this trend to continue, especially given how COVID-19 has accelerated the rate of digital disruption. I believe this bodes well for all of technology.

— Gene Kim, author and founder of IT Revolution

Hybrid product teams will become a pillar of customer value delivery.

With the rise of hybrid (remote/in-office) product teams, upskilling and online training initiatives will expand. As the pressure continues to rise to sell products and services through e-commerce sites, apps, or SaaS solutions, the lines between product and engineering teams will rapidly blur, giving rise to cross-functional, multidisciplinary teams that must learn and grow together. Each member will need to develop a wider combination of process skills, soft skills, automation skills, functional knowledge, and business knowledge, while maintaining deep competency in their focus areas. Product and engineering teams will be measured on customer value delivered, rather than just features or products created.

—Jayne Groll, CEO of the DevOps Institute and author of the 2020 Upskilling Report

Corporate culture will transform as business leaders shift their focus to systems thinking, to drive strategic investments.

Business leaders faced the dilemma of knowing they need to improve time-to-market in order to remain competitive while on a limited budget. Millions of dollars have been spent on digital transformation, which (at best) has yielded local optimizations but not systemic business outcomes. This will drive a focus on applying systems thinking to first identify where and what types of investments will result in delivering desired business outcomes and then scaling these concepts across the organization. 

—Carmen DeArdo, senior value stream management strategist, Tasktop Technologies


CISOs will embrace DevSecOps methodologies.

Cloud-native security will rise higher on the agenda for CISOs as their organizations embrace Kubernetes, serverless, and other cloud-native technologies. It’s a significant cultural shift to embed security within DevOps practices, but it’s necessary: Businesses are moving to the cloud so they can deliver new features quickly and at high frequency, and security teams need to embrace new tools and processes to ensure that these deployments are safe as well as fast.

—Liz Rice, vice president, open-source engineering, Aqua Security

The acceleration of cloud adoption during the pandemic will shift the software security landscape dramatically.

While DevOps represents a clear evolution in the way that software is built, delivered, and operated, the architecture, composition, and very definition of applications will continue to change rapidly, leading to a rethink of software security approaches. These dual pressures of delivery velocity and cloud transformation will have a big impact on software security.

To get ahead of cloud transformation, software security will evolve into a risk-based vulnerability management service that seeks to automate and orchestrate security services as part of the software build-and-delivery pipeline. Security teams will arm developers with “point of capture” tools and coaching to eliminate vulnerabilities during development and provide policy guardrails for enabling speed. Throughout the pipeline, orchestrated security services will automatically reinforce the policy guardrails and enable risk-based vulnerability management for overburdened, under-resourced security teams that are challenged to get in front of cloud adoption. This will result in an increased demand for API security, cloud application security, application security orchestration services, and consolidated, risk-based vulnerability management approaches to software risk reduction.

—Jason Schmitt, general manager of the Synopsys Software Integrity Group

Analytics and automation

Predictive DevOps will be the next transformation that will deliver business value.

This is about using AIOps techniques across the delivery chain to be more efficient in delivering continuous value improvements for the business. To achieve true value, DevOps teams will pivot toward monitoring the business instead of monitoring the application or infrastructure. As a consequence, many dev and ops organizations will realize that they do not have the right skill set to understand what really matters to the business—and the concept of BizDevOps will be born. Business people will become part of the team that delivers digital instead of being a consumer of digital.

—Lars Rossen, chief technology officer, Micro Focus

Operations management

Microservice configuration management will become critical for tracking and deploying logical application versions and microservices across clusters.

Tracking the versions of microservices running across all clusters will become increasingly difficult as organizations embrace Kubernetes. In the process, those organizations will lose the concept of application versions and instead will need to track microservice relationships and configurations cluster by cluster.

To address the challenge, organizations will begin automating configuration management of microservices, versions, and the logical applications they create before deploying them to clusters. Those configuration insights will provide DevOps teams with the data they need to make informed decisions and the confidence to push microservices across dozens of clusters all day long. The bottom line: You will still need the ability to control what you release to your end users. Tracking the configurations and versions of services to application relationships will eliminate the risk and complexity of a microservice implementation.

—Tracy Ragan, CEO and co-founder, DeployHub


Developers will have more say in the technology direction and data strategy of their companies.

Expect an aggressive “shift left” across all industries, where CIO’s will depend more on their development teams to guide the technical direction of the company. Historically, development teams have taken a top-down approach to move their data to the cloud, but, as with many things in the world, that changed with the pandemic and the subsequent reinforcement of cloud-based environments. In 2021, DevOps teams will continue to have far more say in the data strategy process, and as a result we’ll see a greater increase in the mobility of workloads, correlating with an increase in cloud data management techniques.

—Danny Allan, chief technology officer, Veeam

Value streams

DevOps will expand from product delivery to value delivery.

DevOps will expand beyond product delivery to business value delivery and value stream delivery, enabling a broader digital transformation. This requires taking an outside-in view from the business outcomes back into the people, processes, and technologies required to power them. We will see tighter collaboration between business stakeholders and delivery teams, aligning goals and measuring the right business KPIs such as customer satisfaction, usage, and transaction rates, followed by continuous adaptations in processes and technologies to improve them.

—Yaniv Sayers, senior director and chief technologist, Micro Focus

Source: https://techbeacon.com/devops/future-devops-21-predictions-2021