#dataverse accelerator

[ritikay]

Enhance Dataverse with Low-Code Plugins Using Dataverse Accelerator 

Dataverse Accelerator revolutionizes server-side development by enabling low-code plugins using PowerFx instead of traditional .NET-based plugins. This approach allows organizations to extend the Dataverse Web API seamlessly while ensuring faster development, reusability, and easy integration with Power Apps and Power Automate. 

Why Choose Low-Code Plugins? 

Unlike traditional plugins, low-code plugins are stored directly in the Dataverse database, making them easy to manage and deploy. They eliminate the need for manual registration and significantly reduce development effort. Developers can quickly create business logic with minimal or no coding, accelerating app development and automation workflows. 

Key Benefits of Dataverse Accelerator for Low-Code Development 

✅ Faster Development: Build and deploy server-side automation without extensive coding.  ✅ Seamless Integration: Easily connects with Power Apps and Power Automate for enhanced workflows.  ✅ Reusability: Code once and reuse across multiple applications.  ✅ Improved Maintainability: Stored in Dataverse, making version control and updates hassle-free. 

Embrace Dataverse Accelerator to streamline automation, enhance business logic, and boost efficiency with low-code plugins. 

[govindhtech]

Huawei Unveils AI Data Lake Solutions For Smarter Industry

Top Data Lake Solutions

At the 4th Huawei Innovative Data Infrastructure (IDI) Forum in Munich, Germany, Huawei launched the AI Data Lake Solution in April 2025 to accelerate AI implementation across sectors. In his keynote talk, Huawei Vice President and President of the Huawei Data Storage Product Line Peter Zhou addressed “Data Awakening, Accelerating Intelligence with AI-Ready Data Infrastructure.”

Data's importance hasn't altered despite decades of digital upheaval. Zhou stated in his speech: "Be Al-ready by being data-ready. Industry digitalisation advances when data becomes knowledge and information.

The AI Data Lake Solution integrates data storage, management, resource management, and the AI toolchain to help enterprises implement AI. A high-quality AI corpus speeds model training and inference.

Zhou detailed the Data Lake solution's technology and products in his speech:

Continuous performance, capacity, and resilience innovation in data storage

Huawei OceanStor accelerates AI model training and inference. Several AI storage systems perform well. In particular, it helped AI technology company iFLYTEK improve cluster training. Its innovative inference acceleration solution improves inference performance, latency, and user experience to accelerate commercial deployment of large-model inference applications.

Effective mass AI data storage: OceanStor Pacific All-Flash Scale-Out Storage uses 0.25 W/TB and has 4 PB/2 U capacity. It handles exabyte-scale data well, making it perfect for media, scientific research, education, and medical imaging.

Huawei Ocean Protect Backup Storage safeguards oil and gas and MSP training corpus and vector database data. It has 99.99% ransomware attack detection accuracy and 10 times higher backup performance than other popular choices.

Data visibility, manageability, and mobility across geographies

Huawei DME, an Omni-Dataverse-based data management technology, helps companies eliminate data silos in global data centres. DME's ability to access over 100 billion files in seconds helps businesses manage and maximise data.

Pooling various xPUs and sophisticated AI resource scheduling

Virtualisation and container technologies enable efficient scheduling and xPU resource sharing on the DCS platform, increasing resource usage. DME's DataMaster enables AI-powered O&M with AI Copilot in all scenarios. This improves O&M with AI applications including intelligent Q&A, O&M assistant, and inspection expert.

Data Lake Architecture

In a data lake solution, massive amounts of unprocessed, undefined data are stored centrally. This allows flexible processing and analysis of structured, semi-structured, and unstructured data from several sources. Data ingestion, cataloguing, storage, and governance matter.

The following are crucial data lake solution architectural elements:

Data Ingestion: This layer ETLs data from several sources into the data lake. Validation, schema translation, and scrubbing maintain data integrity.

Storage: Blobs or files store unprocessed data. This allows flexible data analysis and use.

Data Cataloguing: This layer helps find, manage, and control lake data. Metadata classification and tagging improve data management and retrieval.

Data processing and analysis in the lake are supported by this layer, which uses Apache Spark or cloud-based services.

The Data Presentation layer prepares data for business users through specified views or dashboards.

Main Ideas

Huawei's AI Data Lake solution blends AI, storage, data, and resources to tackle data-exploitation issues and accelerate AI adoption across sectors.

Data underpins AI

Key takeaway: To be “AI-ready,” one must be “data-ready.” The solution meets the need for high-quality, readily available data for AI research. Prepare data for Al-ready. Industry digitalisation advances when data becomes knowledge and information.

Industry-wide AI adoption acceleration

Businesses may implement AI using the solution's entire platform for data preparation, model training, and inference application deployment.“Designed to accelerate AI adoption across industries” emphasises this.

Key Component Integration

The AI Data Lake Solution integrates resource management, data storage, data management, and the AI toolchain. Not a single product. This integrated method simplifies AI process creation and management.

Addressing Data Issues

It addresses corporate data challenges including data silos (addressed by data management) and the need to handle enormous datasets (resolved by high-capacity storage).

To conclude

Huawei announced the AI Data Lake Solution at IDI Forum 2025 to help organisations optimise data value in the AI future. Huawei's unified architecture, Omni-Dataverse file system, DataMaster AI-powered O&M, and energy-efficient storage solutions provide a powerful, future-ready infrastructure. This solution allows organisations to eliminate data silos, increase data mobility, optimise processes, and accommodate AI workloads for a more intelligent, environmentally friendly, and flexible digital transformation.

[technorucs]

Unlocking the Potential of PowerApps Office 365: A Comprehensive Guide

In today’s fast-paced digital world, organizations are increasingly turning to innovative tools like Microsoft Power Apps to streamline operations and enhance productivity. This pillar page provides an in-depth understanding of PowerApps Office 365, its integration, benefits, and how businesses can maximize its potential. Whether you're looking for insights on development, power apps consulting, or simply curious about the benefits of Microsoft Power Apps, this guide has you covered.

Table of Contents What is PowerApps Office 365? Key Features of Microsoft Power Apps Benefits of Microsoft Power Apps for Your Business PowerApps Integration with Office 365 Use Cases of PowerApps Power Apps Consulting: Why It’s Essential FAQs About PowerApps and Office365

What is PowerApps Office 365? PowerApps Office 365 refers to the seamless integration of PowerApps, a low-code application development platform, with Microsoft’s productivity suite, Office 365. This integration allows users to create customized business applications that leverage Office 365 data, enabling enhanced workflow automation and operational efficiency.

By connecting PowerApps with Office 365, organizations can:

Automate routine tasks. Create tailored solutions without extensive coding. Improve collaboration across teams.

Key Features of Microsoft Power Apps Microsoft Power Apps stands out with its user-friendly interface and powerful capabilities. Here are the top features:

Low-Code Development: Simplifies app creation, making it accessible for non-developers. Integration with Microsoft Ecosystem: Connects seamlessly with Office 365, SharePoint, Dynamics 365, and other tools. AI Integration: Offers prebuilt AI components to enhance application functionality. Data Connectivity: Accesses data from hundreds of sources using Microsoft Dataverse. Responsive Design: Ensures applications are optimized for mobile and desktop use.

Benefits of Microsoft Power Apps for Your Business Adopting Microsoft Power Apps can revolutionize how your business operates. Here are some key benefits:

Cost-Effectiveness: Reduce reliance on third-party software by building custom solutions in-house. Increased Productivity: Automate repetitive tasks, freeing up employee time for strategic initiatives. Enhanced Collaboration: Use data-driven insights from Office 365 for better decision-making. Scalability: Develop applications that grow with your business. Improved User Experience: Create intuitive apps tailored to your team’s specific needs.

PowerApps Integration with Office 365 One of the greatest strengths of PowerApps Office 365 lies in its integration capabilities. With Office365, users can:

Create apps that pull data directly from SharePoint, Excel, and Teams. Automate workflows using Power Automate, a companion tool. Design interactive dashboards that leverage Power BI insights. For instance, a sales team can use ms power apps to create a lead tracking app connected to Office 365 data, streamlining updates in real-time.

Use Cases of PowerApps PowerApps can address a wide range of business challenges. Here are some real-world applications:

Employee Onboarding: Automate HR workflows using apps integrated with SharePoint. Inventory Management: Track and manage inventory seamlessly with custom apps. Customer Service: Create solutions that pull data from Dynamics 365 to enhance customer interactions.

Power Apps Consulting: Why It’s Essential If you're new to Microsoft Power Apps, partnering with a power apps consulting firm can accelerate your journey. Expert consultants can:

Assess your business needs and recommend tailored solutions. Provide training for your team. Help with app design, development, and deployment.

FAQs About PowerApps and Office365 Q: Is PowerApps included with Office 365? A: Yes, most Office 365 plans include basic access to PowerApps, but advanced features may require a premium license.

Q: Can I integrate PowerApps with third-party tools? A: Absolutely. PowerApps supports integration with over 275 data connectors, including Salesforce, Google Drive, and SQL databases.

Q: What is the cost of Power Apps consulting? A: The cost varies based on project complexity and the consulting firm’s expertise. Generally, it’s an investment that pays off through increased efficiency and custom solutions.

Conclusion: Leveraging PowerApps Office 365 empowers businesses to innovate without extensive coding. Whether you're automating workflows, building custom applications, or improving data insights, Microsoft Power Apps is a game-changer. With professional power apps consulting, your business can unlock new levels of productivity and scalability.

[atplblog]

Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] Learn the art of Power Apps with hands-on recipes for development, automation, and AI-powered solutions, combining Power Apps with components of the Power Platform such as Power Automate, Dataverse, Power BI, and Power Pages.Purchase of the print or Kindle book includes a free PDF eBook.Key Features: - Explore how to build apps without custom IT development- Accelerate development with the AI-powered Microsoft Copilot as your virtual app making partner- Create intuitive and responsive interfaces with canvas app UI elementsBook Description: In the rapidly evolving world of low-code development, Microsoft Power Apps stands out as a powerful platform for building custom business solutions. The Microsoft Power Apps Cookbook, 3rd Edition, is your hands-on guide to learning this platform. Through a collection of step-by-step recipes, this updated edition helps you navigate the AI-powered Microsoft Copilot and custom UI elements while empowering you to build efficient and scalable apps.This book emphasizes practical solutions, guiding app makers through building everything from canvas apps to complex data integrations. You will learn how to streamline repetitive tasks using Robotic Process Automation (RPA) and explore how to create external-facing websites using Microsoft Power Pages while handling data management with Dataverse and extending app functionality with the Power Apps Component Framework.Whether you're extending your app's capabilities with custom components or integrating AI features, the Microsoft Power Apps Cookbook equips you with the knowledge and skills to begin your app development journey.What You Will Learn: - Develop responsive apps with Canvas and Model-Driven frameworks- Leverage AI-powered Copilot to accelerate your app development- Automate business processes with Power Automate cloud flows- Build custom UI components with the Power Apps Component Framework- Implement data integration strategies using Dataverse- Optimize your app for performance and smooth user experiences- Integrate Robotic Process Automation (RPA) and Desktop flows- Build secure, scalable, external-facing websites using Microsoft Power PagesWho this book is for: This book is targeted at information workers and app makers wanting to develop custom applications for their organizations or the projects they are undertaking. Traditional app developers will also find this book useful by discovering how to use a rapid application development environment with increased productivity and speed. Readers are expected to have prior exposure to the Microsoft Power Platform ecosystem.Table of Contents- App Maker Basics- Building Pixel-Perfect Solutions with Canvas Apps- Building from Data with Model Driven Apps- Choosing the Right Data Source for Your Applications- Automating Processes with Power Automate- Extending the Platform- Improving User Experience- Power Apps Everywhere- Empowering your Applications with AI Builder- Discovering the Power Platform Admin Center- Tips, Tricks, and Workarounds- Advanced Techniques with the Power Apps Component Framework- Reaching Beyond the Organization with Power Pages [ad_2]

[arammonnews]

Uncover and Accelerate the ROI of Master Data Management (MDM) DATAVERSITY Download the slides here>> This webinar is sponsored by About the Webinar In today’s fast-paced business landscape, achieving quick wins is crucial for proving the value of unified data and master data management (MDM) investments. However, many projects get bogged down with custom development and struggle to tie MDM investments to business outcomes. Join us to learn the secrets of making the business case for MDM and accelerating the time to value for your analytics, operational, and AI initiatives by leveraging AI-ready data.  In this webinar, Karthik Narayan (Director of Product Management) and Anthony Colichio (Senior Value Consultant at Reltio) will share: Strategies and frameworks to show the value modern MDM brings to your business How prebuilt components can help you quickly implement MDM and unify data across multiple domains—including customer, product, and supplier The importance of AI-ready data in enhancing the ROI of your MDM investments for downstream AI initiatives and cloud data warehouses such as Snowflake and Databricks Examples of organizations that have achieved high ROI from their MDM investments Join us to discover how you can jumpstart your data unification initiatives and rapidly drive tangible results. About the Speaker Karthik Narayan Director of Product Management, Reltio Karthik Narayan is a seasoned data management product leader with over a decade of experience in enterprise data management and digital transformation. As Director of Product at Reltio, he oversees the pre-packaged industry solutions that Reltio offers to its customers. Anthony Colichio Senior Value Consultant, Reltio Anthony Colichio is a seasoned value engineer with over two decades of experience in maximizing technology investments. He has held senior roles at Reltio, Acquia, Lithium Technologies, Sprinklr, and Chase with an emphasis in digital strategy, solution engineering, and product integration, excelling in enhancing enterprise technology stacks and aligning business objectives with technical solutions.  Source link #Uncover #Accelerate #ROI #Master #Data #Management #MDM #DATAVERSITY Unlock the potential of cutting-edge AI solutions with our comprehensive offerings. As a leading provider in the AI landscape, we harness the power of artificial intelligence to revolutionize industries. From machine learning and data analytics to natural language processing and computer vision, our AI solutions are designed to enhance efficiency and drive innovation. Explore the limitless possibilities of AI-driven insights and automation that propel your business forward. With a commitment to staying at the forefront of the rapidly evolving AI market, we deliver tailored solutions that meet your specific needs. Join us on the forefront of technological advancement, and let AI redefine the way you operate and succeed in a competitive landscape. Embrace the future with AI excellence, where possibilities are limitless, and competition is surpassed.

[websyn]

Accelerating Digital Transformation with Power Apps Migration 

Digital transformation is about using digital technology to improve how your organization works. Digital transformation is adopting digital solutions and data for business processes and activities to engage customers, optimize operations, transform products, and empower employees. It allows organizations to build infrastructure with flexible capacity per business demands, expanding business models and revenue. Every business eventually needs to reinvent itself in the digital age for survival. 

PowerApps can accelerate digital transformation. It is included as part of the widely used Office 365 platform. It can significantly impact how your business works and is an excellent means of application to accelerate your organization toward digital transformation. PowerApps can automate and support several specific, reasonably simple tasks. Trained employees can use power apps to automatically record information to Dynamics 365 using Microsoft Flow, eliminating the paper and manual data entries for several daily tasks. 

Power Apps can move your organization to digital transformation as you can build custom business apps connecting to your data stored either on Microsoft Dataverse or in various online and on-premises data sources, such as SharePoint, Microsoft 365, Dynamics 365, and SQL Server. You can automate service requests, work on business workflow needs, and enable additional capabilities, like onboarding tasks, team member contact information and forms to complete their internal profiles. and customization for business tasks. 

[nishantrana]

How to – Create an Automated low-code plug-in (Dataverse) (experimental)

In the previous post, where we installed the Dataverse Accelerator app and saw how to write basic low-code Instant Plugins. How to – Create an Instant low-code plug-in (Dataverse) (experimental) Here we’d look at the Automated low-code Plugins. Select the New Plugin option in the Dataverse Accelerator app to create Automate Plugins. We get the option to select the table, the event, define the…

[netmetic]

Key Considerations for Building a Robust Data Strategy

Many business and IT leaders are focused on developing comprehensive data strategies that enable data-driven decision making. A 2016 IDG Enterprise survey found that 53 percent of companies were implementing or planning to implement data-driven projects within the next 12 months—specifically projects undertaken with the goal of generating greater value from existing data.[1] With the growing importance of AI and advanced analytics today, it seems a safe assumption that this number has only increased over time.

The concept of building a data strategy is such a hot topic that top-tier universities are creating executive-level courses on the subject,[2] while industry observers are predicting that by 2020, 90 percent of Fortune 500 companies will have a chief data officer (CDO) or equivalent position.[3]

Yet despite all of this momentum, the concept of a data strategy remains new to many organizations. They haven’t thought about it in the past, so it is uncharted territory, or maybe even an unknown-unknown. With that thought in mind, in this post I will walk through some key considerations for building a robust data strategy.

Why is a robust data strategy important?  A data strategy is a business-driven initiative, and how technology is involved is an important factor.  No matter what, you always start with a set of business objectives, and having the right data when you need it results in business advantages.

The Big Picture

A well-thought-out data strategy will have components specific to one’s own organization and application area. There are, however, important commonalities to any approach. Some of the more important ones include methods for data acquisition, data persistence, feature identification and extraction, analytics, and visualization, three of which I will discuss here.

When I give talks about the data science solutions my team develops, I often reference a diagram describing how many data scientists organize the information flow through their experiments. A good data strategy needs to be informed by these concepts—your choices will either facilitate or hinder how your analysts are able to extract insights from your data!

Figure 1. The standard data science workflow for experimental model creation and production solution deployment. EDA: Exploratory Data Analysis.

Data Acquisition and Persistence

Before outlining a data strategy, one needs to enumerate all the sources of data that will be important to the organization. In some businesses, these could be real-time transactions, while in others these could be free-text user feedback or log files from climate control systems. While there are countless potential sources of data, the important point is to identify all of the data that will play into the organization’s strategy at the outset. The goal is to avoid time-consuming additional steps further along in the process.

In one project I worked on when I was but a wee data scientist, we needed to obtain free-text data from scientific publications and merge the documents with metadata extracted from a second source. The data extraction process was reasonably time-consuming, so we had to do this as a batch operation and store the data to disk. After we completed the process of merging together our data sources, I realized I forgot to include a data source we were going to need for annotating some of the scientific concepts in our document corpus. Because we had to do a separate merge step, our experimental workflow took a great deal more time, necessitating many avoidable late hours at the office. The big lesson here: Proactively thinking through all the data that will be important to your organization is a guaranteed way to save some headaches down the road.

Once you have thought through data acquisition, it’s easier to make decisions about how (or if) these data will persist and be shared over time. To this end, there have never been more options for how one might want to keep data around. Your choices here should be informed by a few factors, including the data types in question, the speed at which new data points arrive (e.g., is it a static data set or real-time transactional data?), whether your storage needs to be optimized for reading or writing data, and which internal groups are likely to need access. In all likelihood, your organization’s solution will involve a combination of several of these data persistence options.

Your choices are also likely to change in big versus small data situations. How do you know if you have big data? If it won’t fit in a standard-size grocery bag, you may have big data. In all seriousness though, my rule of thumb is once infrastructure (i.e., the grocery bag) is a central part of your data persistence solution, one is effectively dealing with big data. There are many resources that will outline the advantages and disadvantages of your choices here. These days, many downstream feature extraction and analytical methods have libraries for transacting with the more popular choices here, so it’s best to base one’s decision on expected data types, optimizations, and data volume.

Feature Identification and Extraction

In data science, a “feature” is the information a machine learning algorithm will use during the training stage for a predictive model, as well as what it will use to make a prediction regarding a previously-unseen data point. In the case of text classification, features could be the individual words in a document; in financial analytics, a feature might be the price of a stock on a particular day.

Most data strategies would do well to steer away from micromanaging how the analysts will approach this step of their work. However, there are organization-level decisions that can be made that will facilitate efficiency and creativity here. The most important approach, in my mind, is fostering an environment that encourages developers to draw from, and contribute to, the open source community. This is essential.

Many of the most effective and common methods for feature extraction and data processing are well-understood, and excellent approaches have been implemented in the open source community (e.g., in Python*, R*, or Spark*). In many situations, analysts will get the most mileage out of trying one of these methods. In a research setting, they may be able to try out custom methods that are effective in a particular application domain. It will benefit both employee morale and your organization’s reputation if they are encouraged to contribute these discoveries back to the open source community.

Predictive Analytics

Again, I think it’s key for an organization-level data strategy to avoid micromanagement of the algorithm choices analysts make in performing predictive analytics, but I would still argue that there are analytical considerations that should be included in a robust data strategy. Overseeing data governance—the management of the availability, usability, integrity, and security of your organization’s data is a central part of the CDO’s role—and analytics is where a lot of this can breakdown or reveal holes in your strategy. Even if your strategy leverages NoSQL databases, if the relationships between data points are poorly understood or not documented, it’s possible that the analysts could be missing important connections, or even prevented from accessing certain data altogether.

Overarching Considerations

To take a step back, a data strategy should include identification of software tools that your organization will rely upon. Intel can help here. Intel has led or contributed actively to the development of a wide range of platforms, libraries, and programming languages that provide ready-to-use resources for data analytics initiatives.

To help with analytical steps and some aspects of feature identification and extraction, you can leverage the Intel® Math Kernel Library (Intel® MKL), Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) and the Intel® Data Analytics Acceleration Library (Intel® DAAL), as well as BigDL and the Intel® Distribution for Python*.

Intel MKL arms you with highly optimized, threaded, and vectorized functions to increase performance on Intel processors.

Intel MKL-DNN provides performance enhancements for accelerating deep learning frameworks on Intel architecture.

Intel DAAL delivers highly tuned functions for deep learning, classical machine learning, and data analytics performance.

BigDL simplifies the development of deep learning applications for use as standard Spark programs.

The Intel Distribution for Python adds acceleration of Python application performance on Intel platforms.

Ready for a deeper dive? Our “Tame the Data Deluge” whitepaper is a great place to get started. For some real-life examples of the way organizations are using data science to make better decisions in less time, visit the Intel Advanced Analytics site.

[1] IDG Enterprise Data and Analytics Survey 2016.

[2] For an example, see Data Strategy for Business Leaders, an educational offering from the Haas School of Business at the University of California, Berkeley.

[3] DATAVERSITY, “2017 Trends in Data Strategy,” December 13, 2016.

The post Key Considerations for Building a Robust Data Strategy appeared first on IT Peer Network.

Key Considerations for Building a Robust Data Strategy published first on https://jiohow.tumblr.com/