Announcing Microsoft Playwright Testing: Scalable end-to-end testing for modern web apps

We are excited to announce the preview of Microsoft Playwright Testing, a new service for running Playwright tests easily at scale. Playwright, a fast-growing, open-source framework, enables reliable end-to-end testing and automation for modern web apps. Microsoft Playwright Testing is a fully managed service that uses the cloud to enable you to run Playwright tests with much higher parallelization across different operating system-browser combinations simultaneously. This means faster test runs with broader scenario coverage, which helps speed up delivery of features without sacrificing quality.

Get test suite results faster

Adding Playwright tests to your continuous integration (CI) workflow helps ensure that as the app evolves, your web app experiences continue to work the way you expect. But as the app becomes more complex, the test suite required for comprehensive testing across multiple browser and operating system combinations also increases in size. This leads to longer test suite completion times, potentially delaying your feature delivery. Development teams are already under pressure to quickly deploy app enhancements. To work around long wait times for test completion, it is common practice for development teams to selectively run only a small subset of tests. In a more detrimental scenario, a team may choose to execute tests less frequently, such as only a few times a week in an integration environment instead of with every pull request. This approach can potentially delay catching issues, complicate the process of pinpointing the cause of problems, and adversely affect the overall productivity of the development team.

With the @playwright/test runner, your tests run in independent, parallel worker processes with each process starting its own browser.  Increasing the number of parallel workers can reduce the time it takes to complete the full test suite. You can set the number of workers using the command line:

npx playwright test --workers=4

However, when you run tests locally or in your CI pipeline, you’re limited to the number of central processing unit (CPU) cores on your local machine or CI agent machine. At some point adding more workers will lead to resource contention, slowing down each worker and introducing test flakiness.

By using Microsoft Playwright Testing service you can increase the number of workers at cloud-scale to much bigger numbers. The worker processes orchestrated by @playwright/test continue to run locally but the browser instances, which are resource-intensive, now run in the cloud. You can see in the demo video below how thousands of tests run on 50 parallel browsers in the cloud managed by Microsoft Playwright Testing, significantly reducing the wait time for test results.

Consistent test results across multiple operating systems and browser combinations

App complexity isn’t the only factor in increasing test suite size. Modern web apps need to work flawlessly across numerous browsers, operating systems, and devices. Testing across all these variables increases the amount of time it takes to run your test suite. With Microsoft Playwright Testing you’ll use the scalable parallelism provided by the service to run these tests simultaneously across all modern rendering engine. This includes Chromium, WebKit and Firefox on Windows, and Linux and mobile emulation of Google Chrome for Android and Mobile Safari. Also, the service-managed browsers ensure consistent and reliable results for both functional and visual regression testing, whether tests are run from your CI pipeline or development machine. This extensive cross-compatibility testing helps ensure your web app delivers consistent performance and functionality across all platforms, optimizing the experience for any user, regardless of their browser or operating system.

Figure 1-Use Microsoft Playwright Testing service from your CI pipelines and code editors.

No test code changes required

If you’re using Playwright today, getting started with Microsoft Playwright Testing is easy! The service is designed to seamlessly integrate with your Playwright test suite, no changes to existing test code required. In just a few steps you can connect your test suite to the service and unlock the full potential of cloud-powered parallel testing. Plus, the service supports multiple versions of Playwright and updates with each new Playwright release, ensuring your tests run against the latest browser versions and technologies while helping to keep your app current, robust, and secure. Now you can focus on thorough application testing without the worry of managing a complex test infrastructure.

Source: microsoft.com

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Manage your big data needs with HDInsight on AKS

As companies today look to do more with data, take full advantage of the cloud, and vault into the age of AI, they’re looking for services that process data at scale, reliably, and efficiently. Today, we’re excited to announce the upcoming public preview of HDInsight on Azure Kubernetes Service (AKS), our cloud-native, open-source big data service, completely rearchitected on Azure Kubernetes Service infrastructure with two new workloads and numerous improvements across the stack.

HDInsight on AKS amplifying performance

HDInsight on AKS includes Apache Spark, Apache Flink, and Trino workloads on an Azure Kubernetes Service infrastructure, and features deep integration with popular Azure analytics services like Power BI, Azure Data Factory, and Azure Monitor, while leveraging Azure managed services for Prometheus and Grafana for monitoring. HDInsight on AKS is an end-to-end, open-source analytics solution that is easy to deploy and cost-effective to operate. 

HDInsight on AKS helps customers leverage open-source software for their analytics needs by: 

• Providing a curated set of open-source analytics workloads like Apache Spark, Apache Flink, and Trino. These workloads are the best-in-class open-source software for data engineering, machine learning, streaming, and querying.
• Delivering managed infrastructure, security, and monitoring so that teams can spend their time building innovative applications without needing to worry about the other components of their stack. Teams can be confident that HDInsight helps keep their data safe. 
• Offering flexibility that teams need to extend capabilities by tapping into today’s rich, open-source ecosystem for reusable libraries, and customizing applications through script actions.

Customers who are deeply invested in open-source analytics can use HDInsight on AKS to reduce costs by setting up fully functional, end-to-end analytics systems in minutes, leveraging ready-made integrations, built-in security, and reliable infrastructure. Our investments in performance improvements and features like autoscale enable customers to run their analytics workloads at optimal cost. HDInsight on AKS comes with a very simple and consistent pricing structure per vcore per hour regardless of the size of the resource or the region, plus the cost of resources provisioned.

Developers love HDInsight for the flexibility it offers to extend the base capabilities of open-source workloads through script actions and library management. HDInsight on AKS has an intuitive portal experience for managing libraries and monitoring resources. Developers have the flexibility to use a Software Development Kit(SDK), Azure Resource Manager (ARM) templates, or the portal experience based on their preference.

Open, managed, and flexible

HDInsight on AKS covers the full gamut of enterprise analytics needs spanning streaming, query processing, batch, and machine learning jobs with unified visualization. 

Curated open-source workloads

HDInsight on AKS includes workloads chosen based on their usage in typical analytics scenarios, community adoption, stability, security, and ecosystem support. This ensures that customers don’t need to grapple with the complexity of choice on account of myriad offerings with overlapping capabilities and inconsistent interoperability.  

Each of the workloads on HDInsight on AKS is the best-in-class for the analytics scenarios it supports: 

• Apache Flink is the open-source distributed stream processing framework that powers stateful stream processing and enables real-time analytics scenarios. 
• Trino is the federated query engine that is highly performant and scalable, addressing ad-hoc querying across a variety of data sources, both structured and unstructured.  
• Apache Spark is the trusted choice of millions of developers for their data engineering and machine learning needs. 

HDInsight on AKS offers these popular workloads with a common authentication model, shared meta store support, and prebuilt integrations which make it easy to deploy analytics applications.

Managed service reduces complexity

HDInsight on AKS is a managed service in the Azure Kubernetes Service infrastructure. With a managed service, customers aren’t burdened with the management of infrastructure and other software components, including operating systems, AKS infrastructure, and open-source software. This ensures that enterprises can benefit from ongoing security and functional and performance enhancements without investing precious development hours.  

Containerization enables seamless deployment, scaling, and management of key architectural components. The inherent resiliency of AKS allows pods to be automatically rescheduled on newly commissioned nodes in case of failures. This means jobs can run with minimal disruptions to Service Level Agreements (SLAs). 

Customers combining multiple workloads in their data lakehouse need to deal with a variety of user experiences, resulting in a steep learning curve. HDInsight on AKS provides a unified experience for managing their lakehouse. Provisioning, managing, and monitoring all workloads can be done in a single pane of glass. Additionally, with managed services for Prometheus and Grafana, administrators can monitor cluster health, resource utilization, and performance metrics.  

Through the autoscale capabilities included in HDInsight on AKS, resources—and thereby cost—can be optimized based on usage needs. For jobs with predictable load patterns, teams can schedule the autoscaling of resources based on a predefined timetable. Graceful decommission enables the definition of wait periods for jobs to be completed before ramping down resources, elegantly balancing costs with experience. Load-based autoscaling can ramp resources up and down based on usage patterns measured by compute and memory usage. 

HDInsight on AKS marks a shift away from traditional security mechanisms like Kerberos. It embraces OAuth 2.0 as the security framework, providing a modern and robust approach to safeguarding data and resources. In HDInsight on AKS authorization, access controls are based on managed identities. Customers can also bring their own virtual networks and associate them during cluster setup, increasing security and enabling compliance with their enterprise policies. The clusters are isolated with namespaces to protect data and resources within the tenant. HDInsight on AKS also allows management of cluster access using Azure Resource Manager (ARM) roles. 

Customers who’ve participated in the private preview love HDInsight on AKS. 

Here’s what one user had to say about his experience. 

“With HDInsight on AKS, we’ve seamlessly transitioned from the constraints of our in-house solution to a robust managed platform. This pivotal shift means our engineers are now free to channel their expertise towards core business innovation, rather than being entangled in platform management. The harmonious integration of HDInsight with other Azure products has elevated our efficiency. Enhanced security bolsters our data’s integrity and trustworthiness, while scalability ensures we can grow without hitches. In essence, HDInsight on AKS fortifies our data strategy, enabling more streamlined and effective business operations.”

Matheus Antunes, Data Architect, XP Inc

Source: microsoft.com

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Microsoft and Adobe partner to deliver cost savings and business benefits

Delivering quality end-to-end digital experiences can be challenging for multiple reasons including lack of resources, legacy technologies, and disorganized customer journeys. Microsoft and Adobe have purpose-built integrations to overcome these challenges with a result of simplifying deployment and reducing overall cost.

Grounded in open software standards and a scalable, secure cloud, Microsoft and Adobe deliver end-to-end technology ecosystems for a modern, secure, and connected enterprise. The integration between our applications transforms data into insights that enable intelligent, targeted, and customized marketing campaigns. With Microsoft setting the data foundation and Adobe providing a comprehensive marketing activation layer, we can take organizations from traditional batch marketing to real-time, precise, and timely event-based marketing.

Marketing needs to be tailored specifically to how, when, and where customers want to shop. To achieve this, Microsoft and Adobe are providing tools to make collaboration between employees easier and smarter, such as digital signatures and document sharing. Meanwhile, automation tools enable organizations to source, adapt, and deliver assets for more personalized customer experiences. These sophisticated integrations between Microsoft and Adobe are enabling businesses to do more with less.

Microsoft and Adobe partner to create a connected enterprise

A 2023 commissioned study conducted by Forrester Consulting—The Total Economic Impact Of Adobe SaaS Solutions with Microsoft Cloud—uncovered how our partnership and technology collaboration with Adobe lowers time and resource costs, improves employee productivity and customer engagement, as well as the return on investment by deploying Adobe applications for the enterprise on the Microsoft Cloud. The study specifically focuses on Adobe Experience Cloud and Adobe Document Cloud running on Microsoft Azure, Microsoft 365, and Microsoft Dynamics 365.

The study explores the challenges organizations hope to address by implementing the integrated Microsoft and Adobe solutions (see image below).

The resounding solutions to these challenges show the strength of the native integration between Microsoft and Adobe SaaS solutions, which enabled organizations to:

• Enhance customer experiences by leveraging consolidated customer data, and with cloud support across solutions, gain real-time insights and analysis about these customers and marketing efforts.
• Strengthen security and protection of data files across the enterprise with tightly integrated tools. With consolidated tools under an all-in-one vendor, tool deployment and IT team management is simplified.

Streamline data access and management across the organization. With tight integrations, organizations can improve collaboration, decision-making, and performance among their teams.

Integrated data means better customer journeys

As customers move between digital channels—mobile apps, social media, online chat, and so on—they generate digital records. Capturing insights from siloed data streams can be a challenge, impacting the ability to create personalized experiences in a timely manner for customers. By transitioning companies from legacy and siloed technologies to a connected, cloud-enabled tech stack, Microsoft and Adobe address the data issues that improve the quality of connections companies have to their customers.

According to the Forrester study, those implementing Adobe SaaS solutions on Microsoft Cloud say that managing customer experiences is the top goal of their organization (71 percent). This is also where organizations found key benefits of implementing integrated solutions.

• Some survey participants saw a 45 percent gain in customer loyalty that they attributed to integrating Microsoft Cloud with Adobe Experience Cloud.
• Data analysis was 15 percent faster, which improved the overall speed of work for marketers by 10 percent.
• Customer satisfaction, the number of transactions, and customer retention rates all increased due to the data-driven capabilities the integration presents.

AI and Machine Learning are pivotal in sifting through large volumes of data, pinpointing areas of importance or anomalies for marketers. The wide range of triggering events, including email, calendar invites, webinars, advertising, data changes, and ERP events offer a holistic view of the customer. A focus on a self-service model delivers speed and ease of use. In combination, organizations gain operational efficiencies, as well as fast, effective marketing from Microsoft and Adobe integrations.

Increase data security across the enterprise

Keeping the organizations’ data and files secure is a leading concern of enterprise leaders—especially with data spread between on-prem servers and the cloud and hybrid work arrangements. The fear is that, without tighter integration with cloud security controls, their data centers are vulnerable to attacks.

Security is inherent with the cloud. Additionally, data stored in Microsoft Cloud and imported directly into natively connected Adobe solutions with native connectors reduces data vulnerabilities. Interviewees of the Forrester study reported that data and files specific to Adobe had not experienced any compromises.

Reduce time and resource demands to streamline deployment

Without the tight integration between Microsoft and Adobe, IT teams—or consultants and contractors—need to code or build custom API connectors to implement solutions. Microsoft and Adobe have developed over 60 specific integrations that are purpose-built for common use cases, reducing the workload for IT teams in customer organizations. The Forrester study found that for the composite organization the integration of Adobe tools went from a multi-month endeavor down to one month and required far fewer resources. This led to overall IT and security team productivity going up by 20 percent. One interviewed company reallocated 15 members of their 40-person IT team to other organizational projects. The Forrester study found that a composite organization based on interviewed customers saw the following financial benefits over three years:

• 251 percent ROI
• 1.3M USD Benefits PV
• 925 Thousand USD Net Present Value

Because Microsoft and Adobe support global content distribution, with Microsoft having cloud storage services worldwide, organizations can increase efficiencies in creating and distributing the millions of content assets needed to create personalized customer journeys.

Source: microsoft.com

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Azure AI Success Stories: Real-World Applications That Inspire

In the realm of artificial intelligence, Microsoft's Azure AI has emerged as a game-changer, revolutionizing industries and reshaping the way businesses operate. Beyond its robust suite of services and tools, Azure AI has left an indelible mark by delivering tangible results across various domains. In this article, we delve into real-world success stories that showcase the transformative power of Azure AI.

1. Healthcare: Early Disease Detection

Azure AI is making significant strides in healthcare by enabling early disease detection. One remarkable example is the partnership between Microsoft and a leading healthcare institution. By leveraging Azure Machine Learning, they developed a predictive model for detecting diseases like cancer at an early stage. This not only enhances patient outcomes but also reduces healthcare costs substantially.

2. Finance: Fraud Detection

In the financial sector, fraud detection is a critical concern. Azure AI has empowered financial institutions to combat fraud with unprecedented accuracy. By analyzing vast datasets in real-time, Azure's machine learning capabilities identify fraudulent activities promptly. This has resulted in a substantial reduction in financial losses and increased trust among customers.

3. Retail: Personalized Shopping Experiences

Retailers are using Azure AI to provide personalized shopping experiences that rival in-store interactions. Through Azure Cognitive Services, these businesses analyze customer preferences and behavior to offer tailored product recommendations. This not only boosts sales but also fosters customer loyalty.

4. Manufacturing: Predictive Maintenance

Manufacturers are optimizing their operations with Azure AI's predictive maintenance capabilities. By analyzing equipment sensor data, Azure predicts when machinery is likely to fail, enabling proactive maintenance. This reduces downtime, lowers maintenance costs, and improves overall efficiency.

5. Customer Support: Chatbots for Instant Assistance

Azure AI-driven chatbots are transforming customer support in industries ranging from e-commerce to telecommunications. These virtual assistants provide instant responses and assist customers 24/7, improving customer satisfaction and freeing up human agents to handle complex issues.

6. Education: Personalized Learning

In the education sector, Azure AI is enhancing personalized learning experiences. By analyzing student data, Azure helps educators tailor lessons to individual needs, ensuring better student engagement and improved academic outcomes.

7. Energy: Optimizing Resource Management

Azure AI is playing a crucial role in optimizing energy resource management. Energy companies use Azure's data analytics capabilities to predict demand patterns and optimize the distribution of resources, reducing waste and environmental impact.

8. Agriculture: Precision Farming

In agriculture, Azure AI is enabling precision farming. By analyzing data from sensors, satellites, and drones, farmers can make data-driven decisions about crop management, leading to higher yields and sustainable farming practices.

9. Entertainment: Content Recommendation

Streaming platforms are utilizing Azure AI to enhance content recommendation algorithms. By analyzing user viewing habits, Azure Cognitive Services suggest content that aligns with individual preferences, increasing viewer satisfaction and retention.

10. Government: Smarter Decision-Making

Government agencies are leveraging Azure AI for smarter decision-making. By analyzing vast datasets, Azure helps policymakers identify trends, allocate resources efficiently, and respond to crises more effectively.

These real-world success stories underscore the versatility and impact of Azure AI across diverse industries. As businesses continue to innovate and adapt, Azure AI remains a trusted ally in their quest for efficiency, accuracy, and customer satisfaction. The journey of AI transformation is not limited to these examples; it's an ongoing exploration of possibilities that Azure AI continues to inspire.

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Microsoft Azure Certifications: Your Path to Lucrative Tech Careers

In today's rapidly evolving digital landscape, the demand for skilled IT professionals is skyrocketing. Businesses across the globe are increasingly relying on cloud technologies to streamline their operations, making cloud expertise a hot commodity in the job market. Among the major players in the cloud computing sphere, Microsoft Azure stands out as a leader, offering a range of certifications that can pave the way to lucrative tech careers. In this comprehensive guide, we will delve into the world of Microsoft Azure certifications and explore how they can open doors to exciting opportunities in the tech industry.

Why Microsoft Azure?

Before we dive into the specifics of Microsoft Azure certifications, let's take a moment to understand why Azure is such a dominant force in cloud computing. Microsoft Azure is a cloud platform that provides a wide array of services, including computing, networking, storage, databases, and more. What sets Azure apart is its scalability, security, and integration with other Microsoft products such as Windows Server and SQL Server. This robust ecosystem makes it a preferred choice for businesses of all sizes.

The Azure Certification Pathway

Microsoft offers a well-structured certification pathway that caters to individuals with varying levels of expertise, from beginners to seasoned professionals. These certifications are categorized into three main levels:

1. Fundamental Certifications

These certifications are designed for those who are new to Azure and want to build a solid foundation in cloud technology.

Microsoft Certified: Azure Fundamentals: This entry-level certification is ideal for individuals who want to understand the basics of Azure, including its services, solutions, and pricing.

2. Associate Certifications

Associate-level certifications are tailored for professionals with some experience in Azure and are ready to take their skills to the next level.

Microsoft Certified: Azure Administrator Associate: This certification validates your ability to manage Azure resources, configure virtual networks, and ensure security and compliance within the Azure environment.

Microsoft Certified: Azure Developer Associate: For developers, this certification focuses on designing, building, testing, and maintaining applications and services on Azure.

3. Expert Certifications

Expert-level certifications are intended for Azure experts who are proficient in implementing advanced solutions.

Microsoft Certified: Azure Solutions Architect Expert: This certification is for architects who design solutions that run on Azure. It covers topics such as infrastructure, security, and governance.

Microsoft Certified: Azure DevOps Engineer Expert: DevOps professionals can validate their skills in implementing continuous integration and continuous delivery (CI/CD) pipelines on Azure.

The Advantages of Azure Certifications

Now that we've outlined the certification pathway, let's explore the tangible benefits of becoming Azure-certified:

1. High Demand for Azure Skills

The tech industry is experiencing a shortage of Azure-skilled professionals. This high demand translates into attractive salary packages and job security for Azure-certified individuals.

2. Diverse Career Opportunities

Azure certifications open doors to a wide range of career opportunities, including cloud architect, Azure administrator, cloud developer, and more. You can choose a career path that aligns with your interests and strengths.

3. Enhanced Skillset

Through the certification process, you'll gain in-depth knowledge of Azure's features and capabilities. This not only makes you more marketable but also equips you with the skills to tackle real-world challenges effectively.

4. Industry Recognition

Microsoft Azure certifications are globally recognized and respected by employers. Holding an Azure certification demonstrates your commitment to excellence and expertise in cloud technology.

5. Stay Current with Technology Trends

The tech industry evolves rapidly, and Azure certifications require candidates to stay updated with the latest Azure services and best practices. This commitment to ongoing learning keeps your skills relevant.

How to Prepare for Azure Certifications

Achieving an Azure certification requires dedication and thorough preparation. Here are some steps to help you succeed:

1. Choose the Right Certification

Select a certification that aligns with your career goals and current skill level. Start with the fundamentals if you're new to Azure and work your way up.

2. Study Resources

Utilize official Microsoft learning paths, online courses, and practice exams to enhance your knowledge. Consider investing in books and joining Azure communities for additional support.

3. Hands-On Experience

Practical experience is invaluable. Set up an Azure account, create virtual machines, and experiment with different Azure services to solidify your understanding.

4. Practice Exams

Take practice exams to assess your readiness. These exams simulate the real certification tests and help you identify areas where you need improvement.

5. Stay Informed

Keep up-to-date with Azure updates and industry trends by following Azure blogs, attending webinars, and participating in forums.

Conclusion

In the competitive tech industry, having Microsoft Azure certifications on your resume can be a game-changer. Whether you're a newcomer looking to break into the field or an experienced professional aiming to advance your career, Azure certifications offer a clear pathway to success. Embrace the opportunity to enhance your skills, boost your earning potential, and become a sought-after IT expert in the ever-expanding world of cloud computing.

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Explore The Top Tips to Crack the DP-203 Exam Here

Microsoft provides a wide range of certifications within nine distinct categories, spanning foundational, role-specific, and specialized skills. Among these certifications, you can find the Azure data engineer certification. DP-203 certification equips professionals with the expertise needed to excel in big data engineering on the Azure platform, enhancing their credibility in the job market.

Benefits of Getting Microsoft Azur Data Engineering DP-203 certified

The Azure data engineer certification instills confidence and competitiveness in the individual who earns it.

Obtaining the DP-203 certification opens up lucrative job prospects for professionals.

It imparts the knowledge of performing interactive queries with serverless SQL pools.

It assists professionals in seamlessly incorporating data from notebooks into Azure Synapse Pipelines.

DP-203 certification empowers professionals to conduct data exploration and transformation within Azure Databricks.

It aids in conducting real-time stream processing using Stream Analytics.

It allows professionals to articulate significant data engineering using Apache Spark.

Microsoft DP-203 Exam Details

The DP-203 exam comprises 40 to 60 questions, including formats like multiple-choice, ordering, scenario-based, single-answer, drop, and others. It lasts 150 minutes and requires a passing score of 700. The exam fee for Microsoft DP-203 is $165, and it is available exclusively in English.

Exam Objectives

Design and implement data storage (15–20%)

Develop data processing (40–45%)

Secure, monitor, and optimize data storage and data processing (30–35%)

What’s the Best Approach to Preparing for the DP-203 Certification Exam?

If you find the available resources and curriculum intimidating or causing stress, this part is specifically designed to address your concerns. Achieving Microsoft certifications isn’t overly complex as long as you have an effective plan. Even better, you don’t require any previous work experience to qualify. All that’s necessary is to enhance your understanding of Azure fundamentals and ensure you grasp the key concepts well.

Before delving into the DP-203 exam syllabus immediately, it’s advisable to take the following approach: Begin by thoroughly reviewing the Microsoft study guide to grasp the exam’s objectives. Afterward, become well-acquainted with the format and organization of the syllabus.

Create a schedule as you work through the DP-203 exam syllabus content. Ensure that you cover all chapters and subtopics comprehensively. If you need more precise explanations, you can always consult Microsoft’s official documentation, videos, and short tutorials on YouTube.

Pause and assess your progress when you’ve completed about half of your preparation. Compare it to the skills and concepts outlined in the prerequisite list. Focus on acquiring a deep understanding of data processing languages, parallel processing, data architectures, and related areas.

The most effective approach to prepare for the prerequisites involves applying your knowledge through practical hands-on labs and utilizing the Azure Sandbox. This allows you to experiment, test, and explore the Azure infrastructure, helping you become acquainted with real-life situations.

Once you feel assured in your preparation, it’s time to take DP-203 practice tests and review sample papers. Use these assessments to evaluate your understanding, identify any areas of weakness or mistakes, and then revisit and reinforce your knowledge in those areas. Afterward, attempt the practice tests again for improvement.

Benefits of Taking a DP-203 Practice Test

Engaging in a practice exam is commonly regarded as one of the most impactful and hands-on methods for getting ready for the Microsoft Azure DP-203 Data Engineering exam. There are numerous advantages associated with taking a practice test, which encompass the following:

1. Gauging your Weak and Strong Areas

Utilizing a DP-203 practice exam can aid you in pinpointing your proficiencies and areas needing improvement across different sections of the exam syllabus. This insight can serve as a valuable guide for concentrating on your studies and enhancing your performance when taking the test.

2. Becoming Familiar With the DP-203 Exam Structure

Engaging in DP-203 practice exams can also aid in acquainting you with the exam’s setup, question formats, and timing. This familiarity can alleviate test-related stress and enhance your confidence when the test day arrives.

3. Devising Methods for Test Execution

Practicing with a test can assist in formulating efficient approaches to taking the exam, such as managing time, maintaining a steady pace, and eliminating incorrect choices. These tactics can enhance your performance when you take the actual test.

4. Calming Test-Related Jitters

Engaging in a practice exam can mitigate test anxiety by creating a feeling of comfort with the test structure and DP-203 exam inquiries. This can increase confidence and a more relaxed mindset on the test day.

Conclusion

Ultimately, the Azure Data Engineering (DP-203) certification is a prime opportunity to demonstrate your abilities and advance your career trajectory. This Azure certification opens doors to numerous career avenues. To attain the DP-203 certification, adhere to the steps outlined above and utilize practice tests on platforms like Edusum to excel in the exam with flying colors.

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Unlocking the potential of in-network computing for telecommunication workloads

Azure Operator Nexus is the next-generation hybrid cloud platform created for communications service providers (CSP). Azure Operator Nexus deploys Network Functions (NFs) across various network settings, such as the cloud and the edge. These NFs can carry out a wide array of tasks, ranging from classic ones like layer-4 load balancers, firewalls, Network Address Translations (NATs), and 5G user-plane functions (UPF), to more advanced functions like deep packet inspection and radio access networking and analytics. Given the large volume of traffic and concurrent flows that NFs manage, their performance and scalability are vital to maintaining smooth network operations.

Until recently, network operators were presented with two distinct options when it comes to implementing these critical NFs. One utilize standalone hardware middlebox appliances, and two use network function virtualization (NFV) to implement them on a cluster of commodity CPU servers.

The decision between these options hinges on a myriad of factors—including each option’s performance, memory capacity, cost, and energy efficiency—which must all be weighed against their specific workloads and operating conditions such as traffic rate, and the number of concurrent flows that NF instances must be able to handle.

Our analysis shows that the CPU server-based approach typically outshines proprietary middleboxes in terms of cost efficiency, scalability, and flexibility. This is an effective strategy to use when traffic volume is relatively light, as it can comfortably handle loads that are less than hundreds of Gbps. However, as traffic volume swells, the strategy begins to falter, and more CPU cores are required to be dedicated solely to network functions.

In-network computing: A new paradigm

At Microsoft, we have been working on an innovative approach, which has piqued the interest of both industry personnel and the academic world—namely, deploying NFs on programmable switches and network interface cards (NIC). This shift has been made possible by significant advancements in high-performance programmable network devices, as well as the evolution of data plane programming languages such as Programming Protocol-Independent (P4) and National Physical Laboratory (NPL). For example, programmable switching Application-Specific Integrated Circuits (ASIC) offer a degree of data plane programmability while still ensuring robust packet processing rates—up to tens of Tbps, or a few billion packets per second. Similarly, programmable Network Interface Cards (NIC), or “smart NICs,” equipped with Network Processing Units (NPU) or Field Programmable Gate Arrays (FPGA), present a similar opportunity. Essentially, these advancements turn the data planes of these devices into programmable platforms.

This technological progress has ushered in a new computing paradigm called in-network computing. This allows us to run a range of functionalities that were previously the work of CPU servers or proprietary hardware devices, directly on network data plane devices. This includes not only NFs but also components from other distributed systems. With in-network computing, network engineers can implement various NFs on programmable switches or NICs, enabling the handling of large volumes of traffic (e.g., > 10 Tbps) in a cost-efficient manner (e.g., one programmable switch versus tens of servers), without needing to dedicate CPU cores specifically to network functions.

Current limitations on in-network computing

Despite the attractive potential of in-network computing, its full realization in practical deployments in the cloud and at the edge remains elusive. The key challenge here has been effectively handling the demanding workloads from stateful applications on a programmable data plane device. The current approach, while adequate for running a single program with fixed, small-sized workloads, significantly restricts the broader potential of in-network computing.

A considerable gap exists between the evolving needs of network operators and application developers and the current, somewhat limited, view of in-network computing, primarily due to a lack of resource elasticity. As the number of potential concurrent in-network applications grows and the volume of traffic that requires processing swells, the model is strained. At present, a single program can operate on a single device under stringent resource constraints, like tens of MB of SRAM on a programmable switch. Expanding these constraints typically necessitates significant hardware modifications, meaning when an application’s workload demands surpass the constrained resource capacity of a single device, the application fails to operate. In turn, this limitation hampers the wider adoption and optimization of in-network computing.

Bringing resource elasticity to in-network computing

In response to the fundamental challenge of resource constraints with in-network computing, we’ve embarked on a journey to enable resource elasticity. Our primary focus lies on in-switch applications—those running on programmable switches—which currently grapple with the strictest resource and capability limitations among today’s programmable data plane devices. Instead of proposing hardware-intensive solutions like enhancing switch ASICs or creating hyper-optimized applications, we’re exploring a more pragmatic alternative: an on-rack resource augmentation architecture.

In this model, we envision a deployment that integrates a programmable switch with other data-plane devices, such as smart NICs and software switches running on CPU servers, all connected on the same rack. The external devices offer an affordable and incremental path to scale the effective capacity of a programmable network in order to meet future workload demands. This approach offers an intriguing and feasible solution to the current limitations of in-network computing.

Figure 1: Example scenario scaling up to handle load across servers. The control plane installs programmable switch rules, which map cell sites to Far Edge servers.

In 2020, we presented a novel system architecture, called the Table Extension Architecture (TEA), at the ACM SIGCOMM conference. TEA innovatively provides elastic memory through a high-performance virtual memory abstraction. This allows top-of-rack (ToR) programmable switches to handle NFs with a large state in tables, such as one million per-flow table entries. These can demand several hundreds of megabytes of memory space, an amount typically unavailable on switches. The ingenious innovation behind TEA lies in its ability to allow switches to access unused DRAM on CPU servers within the same rack in a cost-efficient and scalable way. This is achieved through the clever use of Remote Direct Memory Access (RDMA) technology, offering only high-level Application Programming Interfaces (APIs) to application developers while concealing complexities.

Our evaluations with various NFs demonstrate that TEA can deliver low and predictable latency together with scalable throughput for table lookups, all without ever involving the servers’ CPUs. This innovative architecture has drawn considerable attention from members of both academia and industry and has found its application in various use cases that include network telemetry and 5G user-plane functions.

In April, we introduced ExoPlane at the USENIX Symposium on Networked Systems Design and Implementation (NSDI).2 ExoPlane is an operating system specifically designed for on-rack switch resource augmentation to support multiple concurrent applications.

The design of ExoPlane incorporates a practical runtime operating model and state abstraction to tackle the challenge of effectively managing application states across multiple devices with minimal performance and resource overheads. The operating system consists of two main components: the planner, and the runtime environment. The planner accepts multiple programs, written for a switch with minimal or no modifications, and optimally allocates resources to each application based on inputs from network operators and developers. The ExoPlane runtime environment then executes workloads across the switch and external devices, efficiently managing state, balancing loads across devices, and handling device failures. Our evaluation highlights that ExoPlane provides low latency, scalable throughput, and fast failover while maintaining a minimal resource footprint and requiring few or no modifications to applications.

Looking ahead: The future of in-network computing

As we continue to explore the frontiers of in-network computing, we see a future rife with possibilities, exciting research directions, and new deployments in production environments. Our present efforts with TEA and ExoPlane have shown us what’s possible with on-rack resource augmentation and elastic in-network computing. We believe that they can be a practical basis for enabling in-network computing for future applications, telecommunication workloads, and emerging data plane hardware. As always, the ever-evolving landscape of networked systems will continue to present new challenges and opportunities. At Microsoft we are aggressively investigating, inventing, and lighting up such technology advancements through infrastructure enhancements. In-network computing frees up CPU cores resulting in reduced cost, increased scale, and enhanced functionality that telecom operators can benefit from, through our innovative products such as Azure Operator Nexus.

Source: microsoft.com

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