This article is contributed. See the original author and article here.
Microsoft recognized as a Leader in the Gartner DaaS Magic Quadrant with a global presence, the largest partner ecosystem, and unparalleled integration.
This article is contributed. See the original author and article here.
Sellers are fundamental to any organization’s success—and despite economic headwinds, business leaders are concerned about keeping the talent they have happy and productive at their jobs. Many sellers have long relied on highly manual and disjointed processes that involve a mix of email, spreadsheets, and customer relationship management (CRM) tools. But following manual processes and switching between sales tools and spreadsheets can waste valuable time that sellers need to build relationships with customers and close deals. According to the latest Microsoft WorkLab research, 78 percent of sellers would be happy to have some help from AI to make their everyday tasks—like sending follow-up emails or tracking sales—easier. That is why we’ve been busy building a vision for sales-specific AI to help increase seller productivity and success.
Today, we’re excited to share that Microsoft has been recognized again as a Leader within the 2023 Gartner Magic Quadrant for Sales Force Automation Platforms* for the thirteenth consecutive year. In this year’s report, Microsoft is positioned furthest in Completeness of Vision.
Figure 1: Gartner Magic Quadrant for Sales Force Automation Platforms**
Our strong vision and approach with Microsoft Sales Copilot by fusing collaboration experiences with CRM platform data and generative AI capabilities allows sellers to spend more time focused on engaging with their customers.
Empowering sellers through automation and intelligence
Microsoft Dynamics 365 Sales enables sellers to close more deals and meet customer needs with the help of next-generation AI and real-time insights. Sellers have everything they need in their app of choice to engage with customers, including historical data and access to subject matter experts. Using data, sellers can achieve more consistent sales interactions from creating a lead to closing a sale, predict how much revenue they will generate in a given timeframe, automate repeatable processes and define sales best practices, and promote products and services with targeted marketing campaigns. Additional sales enablement features include adaptive guidance for next best steps based on actionable insights, AI-guided selling features like the sales assistant and conversation intelligence to help build stronger customer relationships, and predictive scoring models to prioritize leads and opportunities for increased conversion and win rates. Sales managers can also get intelligent insights into how their sales team members are performing, so they can provide proactive coaching to improve their teams’ overall performance.
With Microsoft Sales Copilot, which is included with Dynamics 365 Sales Enterprise and Premium licenses, we have established a vision of CRM platform by fusing collaboration experiences with CRM platform data and generative AI capabilities to help sellers reduce mundane tasks and personalize customer relationships even further. Powered by Azure OpenAI Service, Microsoft Sales Copilot features built-in responsible AI and enterprise-grade Azure security. Sellers can access Copilot in the tools where they’re working, whether that’s Outlook, Microsoft Teams, or Dynamics 365 Sales. Microsoft Sales Copilot also connects to Salesforce for instant data syncing. Sellers can use Copilot to automate tasks or view email or meeting summaries, helping them save time on daily tasks and spend more time with customers. AI-powered, real-time insights including customer summaries, recent notes and customer news, and highlights of any issues or concerns help sellers enter customer meetings fully prepared to focus on key items. And to help sellers follow up after those meetings, Copilot can generate AI-assisted content and recommendations, such as customer-specific emails using data from their CRM platforms and Microsoft Graph.
Providing sellers with access to customer data in one place is key to helping ensure their success. Microsoft Dynamics 365 utilizes Microsoft Dataverse to store CRM platform data, which enables customers to securely store and manage data used by business applications. By using a platform solution to simplify and unify sales processes, sellers benefit from products built to talk to each other. Dynamics 365 Sales works seamlessly with technologies including Microsoft 365, Microsoft Power BI, and LinkedIn to enhance and extend capabilities for sellers. This means that sellers can continue to use familiar tools, which helps to simplify user adoption and lower overall total cost of ownership (TCO) and IT costs—a priority for many organizations in today’s economy.
Organizations can leverage the power of the full Microsoft Cloud to help sellers succeed. Dynamics 365 Sales natively integrates with Teams to create open lines of communication for collaborating and aligning on work items across marketing, sales, and service departments. With automatic data syncing between Microsoft 365 apps and Dynamics 365 Sales or other CRM platforms, sellers can also surface customer and opportunity information directly in Teams and Outlook, which minimizes context switching and data loss. In addition, sales operation leads and managers can use Power BI to further analyze trends and build reports. And Microsoft Power Platform enables sellers to automate workflows, create apps, and analyze data to increase agility and innovation.
Helping to ensure our customers’ success
Investec, a global financial services company, set out to help its client-facing teams listen directly to customers and build more valuable relationships. This made conversation intelligence in Dynamics 365 Sales appealing because it automatically transcribes sales calls and analyzes the content, sentiment, and participants’ behavior. Conversation intelligence takes advantage of Microsoft advancements in AI and natural language processing to automatically extract meaningful insights from sales calls. With these insights, Investec can review salespeople’s conversation styles, help coach individuals on best practices, keep track of sales conversations, build stronger client relationships, and ultimately keep track of sales conversations, and build stronger client relationships. With Dynamics 365 Sales, Investec automatically incorporates conversation intelligence data across its customer engagement platform, saving time on manual entry, reducing overhead, and building a comprehensive customer view.
MAPEI, a global leader in adhesive, sealant, and chemical product manufacturing, was using 90 different customized CRM systems across 57 countries when it decided to consolidate into a single, centralized system. Migrating to Dynamics 365 Sales helped MAPEI simplify internal processes for its employees and provide more proactive service to customers. Today, MAPEI salespeople can build strong relationships with customers, make data-driven decisions, and close deals faster. The service also helps salespeople track customer accounts and contacts, track sales from prospect to purchase, and better qualify leads to assure they are spending time on the most impactful opportunities.
Microsoft named a Leader by Gartner
Microsoft is recognized again as a Leader in the 2023 Gartner Magic Quadrant for Sales Force Automation Platforms for the thirteenth consecutive year.
We’re excited to have been recognized as a Leader in the Gartner Magic Quadrant and are committed to providing innovative sales force automation platform capabilities to help our customers accomplish more.
Contact your Microsoft representative to learn more about the value and return on investments, as well as the latest offers—including a limited-time 26 percent savings on subscription pricing for Dynamics 365 Sales Premium.
Source: Gartner, Magic Quadrant for Sales Force Automation Platforms, Adnan Zijadic, Ilona Hansen, Steve Rietberg, Varun Agarwal, Guy Wood, 5 September 2023.
*Gartner is a registered trademark and service mark and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
**This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft.
This article is contributed. See the original author and article here.
Today, we are announcing the general availability of the latest generations of Azure Burstable virtual machine (VM) series – the new Bsv2, Basv2, and Bpsv2 VMs based on the Intel® Xeon® Platinum 8370C, AMD EPYC™ 7763v, and Ampere® Altra® Arm-based processors respectively.
The new generation of Azure burstable B-series v2 VMs are the lowest priced amongst general purpose VMs in Azure and now include native support for Arm-based workloads with the Bpsv2 series. B-series v2 VMs offer up to 15% better price-performance, up to 5x higher network bandwidth, and 10x higher remote storage throughput compared to the previous generation B-series VMs.
Azure customers today can select from a diverse range of Azure virtual machines that are tailored to meet the high CPU performance and utilization needs of their workloads. However, certain categories of workload do not require high levels of CPU utilization and performance on a continuous basis and can be run more cost-effectively on VMs optimized for burstable performance. With B-series v2 VMs, you can balance high CPU utilization and cost savings that automatically meets your workload’s real-time requirements. Burstable virtual machines provide high CPU utilization when applications need it and run at a baseline CPU utilization to save cost when high CPU utilization and performance are not required.
B-series v2 VMs are ideal for workloads that experience unpredictable spikes in demand and require occasional bursts of high CPU utilization. This capability makes burstable VMs ideal candidates for a variety of workloads such as web applications, small and medium databases, micro services, code repositories, CI/CD pipelines for development and test environments, and servers for proof-of-concept development that don’t require full CPU performance all the time, but occasionally need to burst to complete tasks quickly.
With the new Arm-based Bpsv2 VMs now available alongside x86-based Bsv2 and new AMD-based Basv2 burstable VMs, customers can now tailor their infrastructure for specific performance and price-performance requirements across CPU architectures. Arm-Based Bpsv2 VMs, with one physical core per vCPU, are ideal for many workloads like microservices, web apps, containers, and small to medium databases. While Bsv2 and Bav2 VMs can run these workloads, they also offer capabilities and infrastructure for monolithic, vectorized workloads, and others that don’t have affinity to Arm-based VMs.
You can choose from multiple memory ratios for a given vCPU size, giving you the flexibility to select the configuration and architecture that is ideal for your workload. Bsv2-series and Basv2-series offer up to 32 vCPUs and 128 GiB of RAM, and the Bpsv2-series offers up to 16 vCPUs with 64 GiB of RAM. All sizes support accelerated networking and network bandwidth up to 6.25 Gbps.To learn more about the pricing of Arm64-based and x86-based VMs, please visit the Azure Virtual Machines pricing pages.
The new Azure B-series v2 VMs support various Linux OS distributions including Canonical Ubuntu, Red Hat Enterprise Linux, CentOS, Debian, SUSE Enterprise Linux and more. Windows Server and Windows Client are supported on x86-based B-series VMs. Client application developers can take advantage of Azure’s highly available, scalable, and secure platform to run cloud-based software, build and test workflows. To help developers increase their agility and support their work, we’ve made Insider Preview releases of Windows 11 Pro and Enterprise available on Arm-based Azure B-series VMs. Access the full list of images in the Azure Marketplace.
The new virtual machines support all remote disk types such as Standard SSD, Standard HDD, Premium SSD and Ultra Disk storage. To learn more about various disk types and their regional availability, please refer to Azure managed disk type. Disk storage is billed separately from virtual machines and to learn more ondisk pricing please see pricing for disks.
You can also take advantage of Spot Virtual Machines, Reserved Instances and Saving Plan that are available for all new B-series VM families to potentially save even more. You can significantly reduce costs and improve your budget forecasting with Reserved VM Instances through upfront one-year or three-year commitments. With the Azure Savings Plan, you have the flexibility to save across multiple Azure Services, including this one. For workloads that can tolerate interruptions and have flexible execution time, using Spot Virtual Machines can significantly reduce the cost of running in Azure and further optimize your cloud spend. Eligible new Azure customers can sign up for an Azure free account and receive $200 Azure credit.
Start running your applications on Azure B-series v2 VMs today. We can’t wait to hear about the amazing workloads you will build with these new VMs.
Learn what our partners have to say about Azure’s latest burstable VMs:
This article is contributed. See the original author and article here.
Join us at the second annual Power Platform Conference, sponsored by Microsoft, October 1-6 in Las Vegas! Discover, connect, and explore in-person across keynotes, sessions, breakouts, and more – dedicated to Microsoft Power Platform—Power Apps, Power Automate, Power BI, Power Pages, and Power Virtual Agents—and Dynamics 365 applications; and featuring the latest on AI-infused experiences, including Copilot. You don’t want to miss this unique opportunity to learn directly from leaders and community experts!
Learn more below and register today for updates leading up to the event. We look forward to connecting with you in Las Vegas!
Power Platform Conference at-a-glance
WHAT: The 2nd annual Power Platform Conference, featuring more than 100 speakers, 140+ sessions, 18+ workshops, and 35+ exhibitors. This is your opportunity to connect – and reconnect – with users from around the world, build personal and professional relationships, and gain valuable insight into what’s new and next for Microsoft’s business applications – including AI.
WHERE: MGM Grand, Las Vegas, Nevada
WHEN: Kicking off October 1 and 2 with pre-conference workshops, the event runs October 3-5. 2023, ending with post-conference workshops on October 6
WHO SHOULD ATTEND: Whether you’re a business leader, IT pro, developer or consultant, you’ll come away with the knowledge and skills you need to modernize your organization. Join thousands of other Power Platform and Dynamics 365 users, MVPs, technical experts from Microsoft and our partner community to gain deep insights, learn directly from Microsoft engineers, develop new skills to level-up in the workplace, and gain best practices to bring a new level of innovation and efficiency at your organization. You’ll also get a sneak peek at the latest updates and future roadmaps, see live demos, and get a chance to ask questions directly to the experts.
WHAT TO EXPECT: The learning-packed days feature more than 140 activities presented by Microsoft team members, Microsoft MVPs. and community experts—including:
Keynotes from thought leaders at Microsoft, who will provide insight into the future of Dynamics 365 and Power Platform. Featured speakers include Charles Lamanna, Corporate Vice President, Business Applications & Platforms; Thomas Dohmke, Chief Executive Officer, GitHub; Sumit Chauhan, Corporate Vice President, Office Product Group; and Arun Ulagaratchagan
Corporate Vice President, Azure Data.
Specialized sessions designed to deliver skills and best practices that be applied right away, including these sessions for Dynamics 365 users:
This article is contributed. See the original author and article here.
Welcome! This post is going to cover the end-to-end process of configuring and using Workload identity federation in Azure DevOps for your Terraform deployments. We’ll cover:
Why use Workload identity federation
What is Workload identity federation and how does it work
How can your organisation join the Workload identity federation preview
How to configure Workload identity federation using the Azure DevOps Terraform provider
How to use Workload identity federation in an Azure DevOps Pipeline with the Terraform task
Why use Workload identity federation
Up until now the only way to avoid storing service principal secrets for Azure DevOps pipelines was to use a self-hosted Azure DevOps agents with managed identities. Now with Workload identity federation we remove that limitation and enable you to use short-lived tokens for authenticating to Azure. This significantly improves your security posture and removes the need to figure out how to share and rotate secrets. Workload identity federation works with many Azure DevOps tasks, not just the Terraform ones we are focussing on in this article, so you can use it for deploying code and other configuration tasks. I encourage you to learn more about the supported tasks here.
What is Workload identity federation and how does it work
Workload identity federation is an OpenID Connect implementation for Azure DevOps that allow you to use short-lived credential free authentication to Azure without the need to provision self-hosted agents with managed identity. You configure a trust between your Azure DevOps organisation and an Azure service principal. Azure DevOps then provides a token that can be used to authenticate to the Azure API.
You can read more about how Workload identity federation works here.
In the first iteration Workload identity federation for Azure DevOps works within the scope of a Service Connection. A new type of Azure Resource Manager Service Connection is available that enables this:
Any task that supports a Service Connection and has been updated to use Workload identity federation can then use your configured trust to interact with Azure resources.
On the Azure side we need to configure Federated Credentials on an App Registration or User Assigned Managed Identity service principal. There are a few settings needed for this, which you can find in the Azure DevOps Service Connection or you can figure out up front:
Issuer URL: This is the a URL in the format https://vstoken.dev.azure.com/ where organisation-id is the GUID of your Azure DevOps organisation. For example https://vstoken.dev.azure.com/f66a4bc2-08ad-4ec0-a25e-e769dab3b294.
Subject identifier: This is the mapping to your service connection in the format sc://// where organisation-name is your Azure DevOps organisation name, project-name is your Azure DevOps project name and service-connection-name is the name of your service connection. For example sc://my-organisation/my-project/my-service-connection.
Audience: This is always api://AzureADTokenExchange.
Here is what it looks like in the Azure Portal:
Once we have the service connection and federated credentials configured, we can use the service connection to authenticate to Azure. You’ll just need to grant your service principal some permissions in the subscription.
How can your organisation join the Workload identity federation preview
The preview is open to anyone, you can input the name of your Azure DevOps organisation into this form to sign up. Once signed up, you’ll have access to the feature flag and you’ll find it under the organisation ‘Preview Features’ menu. Turn it on and you’ll see the new Azure Resource Manager Service Connection types. You can find lots more information here.
How to configure Workload identity federation using the Azure DevOps Terraform provider
The Azure DevOps Terraform provider has been updated to support the creation of Workload identity federation Service Connections. The documentation can be found here.
The following example shows how to setup a Service Connection and User Assigned Managed Identity with Federated Credentials:
We have to supply the client id of our service principal in the credentials block service connection, so it knows which service principal is configured with federation.
We supply the string WorkloadIdentityFederation in the service_endpoint_authentication_scheme attribute to tell it the type of service connection we want to create.
We use the workload_identity_federation_issuer and workload_identity_federation_subject outputs of our service connection to populate the federated credentials. This is a shortcut for getting this information, which you of course get from elsewhere if you prefer.
Once you run this Terraform and create these resources you’ll be ready to use the Service Connection in your Azure DevOps Pipeline.
How to use Workload identity federation in an Azure DevOps Pipeline with the Terraform task
We have updated the two most popular Terraform Tasks to support Workload identity federation, these are:
The following example shows how to use the Microsoft DevLabs task in an Azure DevOps Pipeline:
jobs:
- deployment: deploy
displayName: Deploy with Terraform
pool:
vmImage: ubuntu-latest
environment: dev
strategy:
runOnce:
deploy:
steps:
- checkout: self
displayName: Checkout Terraform Module
- task: TerraformInstaller@0
displayName: Install Terraform
inputs:
terraformVersion: 'latest'
- task: TerraformTaskV4@4
displayName: Terraform Init
inputs:
provider: 'azurerm'
command: 'init'
workingDirectory: '$(workingDirectory)'
backendServiceArm: '${{ variables.serviceConnection }}'
backendAzureRmResourceGroupName: '$(BACKEND_AZURE_RESOURCE_GROUP_NAME)'
backendAzureRmStorageAccountName: '$(BACKEND_AZURE_STORAGE_ACCOUNT_NAME)'
backendAzureRmContainerName: '$(BACKEND_AZURE_STORAGE_ACCOUNT_CONTAINER_NAME)'
backendAzureRmKey: 'terraform.tfstate'
env:
ARM_USE_AZUREAD: true # This environment variable tells the backend to use AzureAD auth rather than trying a create a key. It means we can limit the permissions applied to the storage account and container to least priviledge: https://developer.hashicorp.com/terraform/language/settings/backends/azurerm#use_azuread_auth
- task: TerraformTaskV4@4
displayName: Terraform Apply
inputs:
provider: 'azurerm'
command: 'apply'
workingDirectory: '$(workingDirectory)'
commandOptions: '-auto-approve -var="resource_group_name=$(AZURE_RESOURCE_GROUP_NAME)"'
environmentServiceNameAzureRM: '${{ variables.serviceConnection }}'
env:
ARM_USE_AZUREAD: true
Right now you are probably thinking “how is this any different to what I do now?” and you’d be right to think that because there is no difference. The Workload identity federation implementation is abstracted away into the choice of Service Connection type. The task knows based on the type of Service Connection how to authenticate to Azure and set the relevant environment variables for you.
If you want to know how you can do it yourself outside of the terraform task, you can see an example of using the Azure CLI task here and here.
Conclusion
We’ve shown you how to configure and use Workload identity federation for Azure DevOps, we want you to SIGN UP FOR THE PREVIEW and start using it right away.
This article is contributed. See the original author and article here.
Introducing the new Get Data Experience: Streamlining Your Data Ingestion Journey
We’re thrilled to unveil a brand new Get Data Experience – the next generation evolution of Ingestion Experience in Azure Data Explorer (ADX)! Designed with simplicity and efficiency in mind, this update streamlines the way you bring data into ADX. Whether you’re a seasoned data professional or just starting your data exploration journey, this new user experience is crafted to empower you every step of the way.
Supporting various auth methods for clusters’ connections
By allowing users to set up connections with alternative credentials, we now provide seamless experience for managing clusters from various user accounts or Azure AD directories.
When adding new cluster connections, if you have multiple user accounts and want to authenticate with a different account, or your account is linked to multiple Azure AD directories, you can follow the optional steps described in this document to create the connections.
Once connected, you can switch between clusters associated with different credentials within a unified interface, without a need to repeatedly sign in and sign out or switch directories.
In the above image The HomeCluster connection uses different credentials from those of the signed-in user, as indicated by the small icon of a person in the upper-left corner.
Heatmap – new visual in ADX dashboards
We’re happy to let you know that we just released a new Heatmap visual.
Heatmaps are graphical representations of data using color. They are most commonly used to provide a clear view of numerical values.
To learn more about heatmap visualization in ADX dashboards, read this document
This article is contributed. See the original author and article here.
For the month of September, we’re exploring learning resources to help you get started with Azure AI and Microsoft Copilots. Get an overview of copilots (AI assistants) available from Microsoft, get started with Azure OpenAI Service, and learn about Azure tools for building intelligent apps and working with machine learning.We also have updates about upcoming events, including details about .NET Conf and Microsoft Ignite.
Use Microsoft Copilots Microsoft offers a variety of copilots (AI assistance in the apps you already use). Check out GitHub Copilot, Microsoft365 Copilot, and Copilot in Viva Sales and discover what each can do for you.
GitHub Copilot documentation Get feedback and predictions from an AI pair programmer as you code. Here’s everything you need to know to get started with GitHub Copilot.
How Microsoft 365 Copilot works Curious about Microsoft 365 Copilot. Watch a demo to see how it works. See how Copilot can write a new business proposal, generate meeting summaries, and identify trends in your data.
Use Azure OpenAI Service Check out this collection of resources to learn everything you need to know to start using Azure OpenAI to build intelligent solutions. Explore Microsoft Learn resources to learn about generative AI, responsible AI, and prompt engineering.
Introduction to prompt engineering Do you know these best practices for working with prompt-based models? This Microsoft Learn documentation offers an intro to prompt engineering and advice for constructing prompts.
Develop Generative AI solutions with Azure OpenAI Service Learn how to develop generative AI solutions with Azure OpenAI. This Microsoft Learn path covers everything from natural language solutions and prompt engineering to working with DALL-E and tapping into your own data.
Build apps and custom models Explore curated resources from Microsoft Learn to help build your Machine Learning skills. Discover Azure tools to help you build apps and work with custom models.
Create computer vision solutions with Azure AI Vision Creating computer vision solutions is easier than you think. Start this Microsoft Learn path and discover how to use Azure Cognitive Services in common computer vision scenarios.
AI Builder documentation You don’t need coding and data science skills to build AI solutions. Delve into AI Builder documentation and discover a low-code path to intelligent apps in your organization.
Azure AI services documentation Build cutting-edge, market-ready applications with AI. Explore Azure AI services, including Azure OpenAI, Custom Vision, Bot Service, Azure Cognitive Service, and more.
AI learning and community hub Power your AI transformation with the Microsoft Cloud. Explore the AI learning and community hub to build AI skills, learn from experts, and find upcoming events.
Events and other highlights
Microsoft Ignite Registration for this year’s Microsoft Ignite is now open. Join us November 14–17, 2023 to explore the latest innovations, learn from experts, and level up your skills. Register now.
.NET Conf 2023 Save the date for .NET Conf, November 14–16. Join this free, 3-day digital event to watch live sessions, get answers from .NET experts, and celebrate the launch of .NET 8.
Azure Developers – Python Day Take your cloud dev skills to the next level. Watch the Azure Developers – Python Day event, available on demand, to learn cutting-edge skills and explore features in Azure designed specifically for Python developers.
Enterprise-grade Reference Architecture for JavaScript Explore this popular reference architecture for JavaScript, available on GitHub. This repo includes architecture patterns, best practices, and functional components that can be used to build modern JavaScript apps on Azure.
This article is contributed. See the original author and article here.
Overview
Moving to Cloud is a crucial part of digital transformation as business grows. As a leading Cloud provider, Azure provides intuitive, guided, and repeatable support for migrating an on-prem application to Azure cloud. You can learn more at Reliable web app pattern – Azure Architecture Center.
To ensure a web application runs performantly and resiliently, you can use Azure Cache for Redis. This blog zooms into caching best practices and guidance in the Reliable Web App Pattern.
Moving In-Memory Cache to Distributed Cache in Azure
Suppose your online order web app saves shopping cart items in a user’s session data in memory. As you migrate a web application from on-prem to Azure Cloud, caching in-memory no longer works because the virtual servers may scale-in or shut down to save cost. In such cases, in-memory data will be gone. Distributed caching persists cached data by decoupling cache from the web server instances to support scalability and resiliency. In addition, if you were self-hosting Redis Cache, re-platform to use Azure Cache for Redis removes operational overhead from self-hosting. Azure resources located within the same data center have negligible network latency, and the in-memory nature of Azure’s Cache service ensures accessing data is fast.
Azure Cache for Redis is the 1st party caching solution in Azure. Developed in partnership with Redis Inc, Azure Cache for Redis provides the unparalleled up to 99.999% SLA with Enterprise SKUs, data replication across geographic locations, and scenario-drive cost effective hosting options to meet your business needs.
Patterns and Best Practices to Use Distributed Caching in Azure
To help customers successfully re-platform from on-prem to Azure, the Reliable web app pattern offers reference architecture scenarios to guide the process. Currently, the Reliable Web App Pattern for .NET and Java are available. Without losing generosity, we will look at the recently announced Java support for how to apply caching when migrating an application.
We recently announced GA for the Java version of Reliable Web App pattern, which is a set of architectural recommendations and guided, repeatable process with Reference Architecture applications to ensure customers can successfully migrate to Azure. You can learn more at Reliable web app pattern for Java – Plan the implementation – Azure Reference Architectures. In the example reference application, an online training web app written in Java Spring Framework is transformed to run on Azure. Figure 1 shows a screenshot of the web application, which is a real-world and production-ready training web application that shows a series of educational tutorials. The playlist of video tutorials might be different for each user and can be cached for quick access and saving a user’s training progress.
Figure 1: Java sample application homepage that shows training video playlist
In the reference architecture caching is applied to multiple scenarios such as logged in user settings, security settings, and displaying the playlist on the homepage as shown in Figure 1. Let’s look at how smooth it is to transition from local cache to Azure with our example.
Per Figure 2, initializing Redis in a Java application only takes a few lines of code, and the configuration is very flexible. You can find it at RedisConfig.java.
Figure 2: Initializing Redis client in a Java Spring web application.
Java Spring Caching abstraction allows tag annotations to be used for indicating if a method’s return value should be cached and how. In Figure 3, the tag annotations hide all the details on interacting with Redis Cache and allows developers to focus on business logic. Referring back to the video playlist shown in Figure 1 above, in its implementation from the PlaylistService.java, the playlist and playlist per user are cached for saving user progress and performantly and query-efficiently loading the result next time.
Figure 3: Java Spring Caching Abstraction for caching method return results.
Deploying the application to use Azure Cache for Redis is straight forward. Just point the app configuration to the right Azure Cache for Redis endpoints to get your app working! Follow the README.md instructions to deploy the app.
Use the Cache-Aside pattern for data consistency
As a user’s playlist is part of the session data and is saved in cache, what if the overall playlist gets updated? How do we make sure information in cache stays consistent with the backend database?
The cache-aside pattern guarantees application data consistency. It would attempt to read from cache every time, upon a cache miss it reads from the database and put the value in cache for the next read, and upon an update in database all related cache records will be cleared to keep data up-to-date. Figure 4 illustrates how the cache-aside pattern keeps data from cache to the backend data store to be consistent. You can learn more at Cache-Aside pattern – Azure Architecture Center.
Figure 4: cache-aside pattern keeps cache and datastore consistent
Referring to our Java sample application, the Spring Caching Abstraction hides the details as this algorithm is a highly repeatable process.
This article is contributed. See the original author and article here.
In today’s fast-paced sales landscape, staying ahead of the game is more crucial than ever. Sales teams aiming to close deals swiftly understand the importance of real-time opportunity information. But let’s be honest, obtaining a straightforward Opportunity summary can be quite the challenge. Vital details are often scattered across various applications and documents, making it a time-consuming endeavor to compile a concise and up-to-date opportunity snapshot. However, there’s a game-changer now available within Microsoft Sales Copilot, and it comes in the form of AI-powered Opportunity summaries. In this blog, we’ll dive into how deal teams can revolutionize collaboration and keep the focus on valued customers. Say goodbye to information overload and hello to seamless teamwork!
Stay informed: AI-generated Opportunity summaries
Sales Copilot makes it easy for sales team members to stay up to date on opportunities by providing AI generated summaries of the latest opportunity information. Sellers see this information in a Deal room Collaboration space in Microsoft Teams, which is set up to collaborate and stay connected on opportunity-related activities. The Opportunity summary includes details such as sales stage, budget, closing date, and latest activity saved in opportunity notes. These summaries not only provide time saving benefits, but also help the sales team gain a shared understanding of the opportunity status. This shared perspective enhances their ability to collaborate effectively, particularly when addressing customer needs and managing deals efficiently.
Enhancing team collaboration: comprehensive access to opportunity details
For Dynamics or Salesforce organizations that have created a Deal room in Microsoft Teams, the Opportunity summary is instantly displayed after setup. Members of the deal team will find a complete summary that shows information from relevant CRM opportunity fields like sales stage, budget amount, estimated close date, parent account name, primary contact name, and more. Any important notes added to the opportunity are also included in the latest activity summary.
AI generated Opportunity summary shown on setup of a Deal room
This AI-generated summary offers the sales team a clear overview of the opportunity and helps them save a lot of time and work better together, focusing on the most important things with the latest information available.
Whether new team members join the deal room or existing ones seek real-time updates, accessing this summary is effortless. By issuing the command “@Sales Copilot show Opportunity summary” within Deal rooms, or alternatively, by generating the Opportunity summary through the “@Sales Copilot Help” options, individuals can promptly tap into the most pertinent details of an opportunity.
Activate AI summaries via Sales Copilot admin settings
Dynamics and Salesforce administrators can enable AI-generated Opportunity summaries in Deal rooms by accessing the Sales Copilot admin settings in Sales Copilot app in Microsoft Teams. Within the admin settings, simply toggle on the Copilot option in the ‘Set up Copilot AI features’ page.
Once the Copilot toggle is enabled, the Opportunity summary is generated using the following CRM fields from the opportunity record:
Opportunity name
Opportunity ID
Created On
Estimated close date
Sales stage
Budget amount
Description
Parent Account name
Primary contact name
Content under the Latest activity section is generated from the summary of the last three notes added to the opportunity record.
Admin settings to enable AI generated Opportunity summaries
This article is contributed. See the original author and article here.
In today’s fast-paced business world, embracing technology and prioritizing employee engagement are key to staying ahead. Sage, a global leader in payroll, HR and finance software, understands this well. Their partnership with Microsoft Viva Glint and Viva Insights is a compelling example of how utilizing a comprehensive employee listening strategy can drive business performance within organizations.
Delivering exceptional customer service and prioritizing engineering time is critical for Sage to stay ahead in a competitive market. Sage addressed challenges to retention and productivity by understanding the voice of the employee and meeting patterns. With this knowledge in hand, leaders at Sage were able to address root causes and improve retention by 30% in their customer service department, improve engineering productivity by 10% by reducing unnecessary meetings and improve customer service performance by 10%.
Take a look at the full customer story here and video highlights below.
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