azure integration services presentation

Design Guide: Azure Integration Services

A quick guide to key design areas for azure integration services.

Azure Integration Serives (AIS) offer a modern hybrid integration solution. They integrate apps, data and processes across on-prem and cloud environments.

The basic description and use cases of those services can be found in this whitepaper .

To help create well-architected AIS solutions, this guide will highlight:

• The main components and interactions of an AIS solution
• The key design areas and decisions for AIS
• Key demos to help teams get started

1. AIS in motion

Provision infrastructure, policy and config as code [cloud engineers].

• Cloud Engineers provision infrastructure resources; ideally through code: ARM, Bicep or Terraform templates
• Infrastructure resources include but not limited to API Management instance, Logic Apps environment, Service Bus Namespace, Azure Monitor dashboards and alerts
• Azure Policies are created to govern usage. For example, one policy can block access to specific connectors like Dropbox. Another policy could restrict network access to workflows or APIs through private endpoints only
• Infrastructure defined as code has settings and parameters set per environment (dev, staging and prod environment)
• Permissions and AAD roles are defined for API Developers, App Developers, Integration Specialists and possibly partners

Build and Publish APIs [API Developers]

• API Developers are ideally given access to Dev or Pre-Prod API Management instances
• They author APIs through IDEs or using Azure Portal
• They publish their APIs to the Prod API Management instance through Git Pull Requests or through pre-defined permissions to specific APIs
• A CI/CD pipeline pushes the API definitions and policies into production passing through any needed approvals or Pre-Prod environments. The pipeline definition can be created by Cloud Engineers

Build Workflows [Integration Specialists]

• Integration Specialists build Logic Apps workflows through code or visually in IDE or Azure Portal. More details can be found here
• After local testing they push their workflows to a Git repository
• A CI/CD pipeline pushes the workflows through checks and gates passing by Pre-Prod environment into a Prod environment. The pipeline definition can be created by Cloud Engineers
• Workflows use connectors but also can consume organization APIs or communicate to other systems through Messages or Events
• Besides workflows, applications and systems can be integrated directly through queues, topics and events. However, adding Logic Apps workflows to the mix offers convenience of message pushing rather than polling, as well as low-code processing
• Integration Specialists can use Azure Monitor to monitor and get alerts on the system and business operations. This is in addition to pre-defined dashboards and alerts created through Cloud Engineers’ templates. More details can be found here
• More details on Logic Apps Standard can be found here

Discover and use APIs [Partners and App Developers]

• App Developers in the organization or in a partner organization might need access to specific APIs
• The APIs defined by API Developers are published to a Developer Portal
• App Developers can discover APIs, understand request and response payloads and try out the APIs
• App Developers consume their APIs in their applications
• Integration Specialists also discover and use APIs in their workflows
• App Developers can integrate their apps through messaging technologies as well as APIs

Provide a secure Landing Zone [Platform Engineers]

• All the above integration activities ideally run within an Integration Landing Zone provisioned and managed by Platform Engineers
• The Landing Zone has identity management controls (e.g. MFA and RBAC), org wide policies (e.g. encryption and tagging), connectivity (e.g. VPNs and firewalls) and more
• It has all the guardrails and controls to control spend, security, compliance and more
• Platform Engineers are part of a central team that operates several other landing zones for data, cloud native apps, infrastructure…etc.

2. Key Design Areas

Resource organization and centricity.

• Easier to maintain and monitor
• Can hit platform limits faster
• Some roles or resources can introduce bottlenecks or single points of failure
• Provides more agility and autonomy to app/business teams
• Clear ownership of data and integrations by app/business teams
• Might be an overkill for a small organization or low number of integration workloads
• Requires more standardization and automation across app/business teams

Security and Roles

Consideration: Will the “Ops” roles like Cloud Engineer and “Dev” roles like Integration Specialists be part of the same team?

• Usually the organization DevOps maturity level can play a role in defining teams structure and responsibilities

Consideration: Will the “Ops” roles be replicated across team in case of a hybrid/decentralized environment?

Consideration: Do I have members with skills to play more than one role simultaneously?

Consideration: What RBAC permissions will be given to each role and what system identities (Managed Identities) are needed?

Consideration: What authentication mechanisms will be used: OAuth2, Managed Identities…etc.?

Connectivity

More details on Logic Apps networking can be found here

Consideration: What are the network access requirements? Is all traffic coming from on-prem or from with Azure?

Different Azure technologies will help in each network access use case:

Private Endpoint: Workflows or APIs expose private IP endpoints

Private DNS: Because some endpoints expose private IPs, a DNS resolution mechanism from within Azure or on-prem is needed

VNET integration: If workflows or APIs require access to virtual network resources, they need to be injected into the same or a peered virtual network

VPN or ExpressRoute: How are on-prem and Azure networks connected?

Firewall: Do I need to reach Azure endpoints or egress to internet/on-prem through firewalls?

Compliance, Governance and Design Patterns

Consideration: Do we have compliance requirements to isolate workloads (within an ASE: App Service Environment) from other cloud tenants?

Consideration: Do we have a list of automated governance policies that get reviewed regularly?

Consideration: Do we have teams trained and in agreement on Cloud Messaging Patterns and how to select the right integration technology?

Observability

Consideration: Do our technical teams have enough visibility into systems and integration workloads health?

Consideration: Do business teams have the right access into business activities health with an option to re-submit or retry failed workflow?

Consideration: Do we have the right alerts and are they reaching the right audience, through the right channel, and at the right frequency?

3. Key Demos

API Management DevOps Resource Kit

Logic Apps (Workflows) DevOps - Credits: Bec Lyons

Logic Apps Observability - Credits: Paco de la Cruz

Enterprise Scale: App Service Environment - Credits: Cynthia Kreng

Enterprise Scale: API Management - Credits: Cynthia Kreng

iPaaS Enterprise Starter - Bicep

Feedback and Contribution

For feedback and contributions, please open a GitHub issue

How I use Azure Integration Services on Cloud With Chris

I’ve written blog posts previously around Azure Service Bus vs Azure Storage Queues, as well as an introduction to Azure Logic Apps and how I used it at the time. Back then, my use-case was fairly rudimentary and focused on a specific scenario. In this blog post, I explain the changes that I have made and how I’ve used common cloud design / integration patterns to implement a more robust solution.

Why was a revamp needed of the Cloud With Chris integration platform?

I’ve written blog posts previously around Azure Service Bus vs Azure Storage Queues , as well as an introduction to Azure Logic Apps and how I used it at the time. Back then, my use-case was fairly rudimentary and focused on a specific scenario. In this blog post, I explain the changes that I have made and how I’ve used common cloud design / integration patterns to implement a more robust solution.

So let’s first establish the original problem statement and how it has evolved. I required a solution to cross post content (e.g. Azure Blogs, Azure Updates, Cloud With Chris Blogs) with some kind of personal message across several social platforms. Initially, my scope was to post on these social networks immediately, or to queue messages using an external service called Buffer.

Longevity and robustness was not the primary objective. The objective was a quick-and-dirty initial proof of concept to unblock my immediate needs of cross posting content with custom comments, including the URLs to the original content. As I designed the initial solution to solve a targeted use-case, it quickly became apparent that a more robust solution was needed. I wanted to take multiple actions based upon a new item of content, and also use a custom URL shortener, so that I can track metadata about the content in Google Analytics (understanding where my content was primarily being consumed from, so I can continue growing my audience).

This lack of long-term thinking meant that I accumulated technical debt quickly (no surprises there!). Unsurprisingly, I had to re-architect the solution to address that I had a mix of technologies/implementations in place, to solve for the evolving challenges and needs. This wasn’t scalable and this wasn’t sustainable. Hence, the need to re-architect the solution.

The next iteration of the architecture

This time around, I opted to do some up front planning on the long-term vision for the design, and my requirements. I’ve said it on the channel several times previously; failing to plan is planning to fail. During this process, I went through several iterations of an initial design. The most recent version looks similar to the below.

Let’s begin by describing the main phases of the integration process flow.

Summarising the overall flow

Ingestion Phase - Each source (e.g. Individual RSS Feed such as Azure Blogs, Azure Updates, Cloud With Chris Episodes, Cloud With Chris blogs, etc.) has a Logic App which is checking for new blog posts. Once a new blog post is detected, an object is created in Azure Table Storage (including item title, item type [e.g. blog, episode, azureblog, azureupdate, azuredevopsblog, etc.], summary, etc).

Approval Phase - A Single Page Application is used to render the pending list of items to be approved. These content items can be processed individually (i.e. approved/rejected). An approval object can contain multiple actions.

The flexibility among approvals is the key difference and ‘magic’ to my future extensibility. Each content item can have many actions associated with them.

• An action can have one or more action types. At time of writing, these are immediate, scheduled and roundup.
• An action can be associated to one or more platforms. At time of writing, these are Facebook, LinkedIn, Twitter and Reddit.

If a message is approved, it is sent to an Azure Service Bus topic and deleted from the Azure Table Storage.

If a message is rejected, it is deleted from the Azure Table Storage.

Note: I am considering making an adjustment to this functionality. instead of deleting the record from Azure Table Storage (in either approval or deleted), it should perform an update instead. That way, I can keep a log of all items of content previously ingested. That allows me to see a history of approvals/rejections, but also allows me to re-post content in the future if I wish.

Processing Phase - Messages are posted into a Service Bus Topic called Actions . In the future, this Topic will have several subscriptions (Which act as my extensibility point). At time of writing, there is a subscription called Immediate and another called Schedule .

• The Immediate subscription has a filter for a custom property actionType matching immediate.
• The Schedule subscription has a filter for a custom property actionType matching schedule.
• There is a specific Logic App deployed for each subscription. For example, there is a Logic App to process messages picked up by the Immediate Subscription. There is another Logic App to process the messages picked up by the Schedule subscription.

Cloud Design Patterns adopted within this architecture

Let’s run through a few of the key Cloud Design patterns used in this architecture.

This architecture heavily uses the pipes and filters pattern .

• In a nutshell, this pattern is when we breakdown complex processing into a number of distinct tasks. That means that these individual processing tasks can be deployed and scaled independently.
• As an example, I had a separate process for Azure Blogs/Updates, to my Cloud With Chris blog, which would all effectively use the same process. This is in essence, the description under the Context and Problem section of the Azure Architecture Center doc.
• The current iteration relies upon a consistent messaging format being passed between the various stages of the processing pipeline. That way, the individual processing stages can be decoupled and work independently of each other, allowing flexibility and ease of extensibility.
• You can find out more about the Pipes and Filters pattern in my Architecting for the Cloud, One Pattern at a time series with Will Eastbury .

This architecture also leans on the Publisher Subscriber pattern . You may have also heard of this as the Pub/Sub pattern.

• This was more of a workaround / ‘art of the possible’ implementation, as I didn’t have access to the Azure connections in Power Automate. As Power Automate was being used for my ingestion of new blogs/episodes (due to the Approvals functionality in Power Apps, which was needed for custom comments in posts), Microsoft Teams was being used as a workaround as a messaging bus. In summary, it allowed for ‘decoupling’, but didn’t give me a true Publish/Subscriber approach, with all the benefits that I’d want to leverage.
• If you follow the Azure Docs Cloud Design Pattern definition, you’ll see that it describes subscribers as the ability to have multiple output channels for a message broker. That’s exactly what I’ve implemented for the latter phase of the architecture.
• It could be argued that a Publisher Subscriber pattern being used in the first phase of the architecture too. The array of Logic Apps per content source are the senders. The Approvals Single Page Application is the receiver/consumer. The Azure Table Storage is being used as a pseudo message broker. While it’s not a typical broker/bus (e.g. Azure Storage Queue / Azure Service Bus), I chose Azure Table Storage as it’s able to persist the messages until they are approved. Ordering of the messages isn’t a requirement, but longevity of the records is of higher importance to me, and being able to query through the full list.
• You can find out more about the Publisher Subscriber pattern in my Architecting for the Cloud, One Pattern at a time series with Will Eastbury .

Technologies adopted within this architecture

Some technologies are re-used within this architecture from the first iteration (Logic Apps), while there are several technologies introduced (Azure Table Storage, Azure Functions and Azure Service Bus).

• Azure Logic Apps is used as the processing engine to detect new content in RSS feeds. It’s also used to listen to the various Azure Service Bus Topic Subscriptions and take appropriate action.
• Azure Table Storage is used to persist the content which is pending manual approval from the user.
• Azure Functions is used to generate the Static site dynamically. The Durable Functions capability of Azure Functions is used to take an approved message, retrieve a shortened URL and transform the message to be sent to the Service Bus Topic, as well as sending the actual message.
• Azure Service Bus is used as the main messaging mechanism. The standard SKU of Azure Service Bus is being used, as it unlocks the Topic / Subscription functionality, which is what enables the routing to an appropriate Logic App processor, allowing for future extensibility of the system.

Exploring the Ingestion Phase

The ingestion phase is responsible for kickstarting the entire integration process. This is triggered by a new item being available in an RSS Feed. This could be a Cloud With Chris RSS Feed (e.g. RSS feed for episodes, RSS feed for blogs) or external content that I want to share on social media (e.g. RSS Feed for Azure Blog, Azure DevOps Blog, GitHub Blog, etc.).

I opted for Azure Logic Apps in this scenario, as it enabled me to pull the needed workflow for this aspect very quickly. There is a trigger in Azure Logic Apps to trigger when a new feed item is published .

Once the workflow has been triggered, I needed to do something with that content item. Originally, I had thought about using an Azure Storage Queue or Azure Service Bus Queue to decouple the triggering of items from the approval step. However, as I thought through this further - it didn’t quite feel like the best fit.

Azure Storage Queues / Azure Service Bus Queues are great examples of messaging services that you could use in Azure. Given that they are messaging platforms, each message will typically have a TTL (time-to-live) and sit in the queue to await processing by a consumer. If you’re interested in the differences between these two options, you can find more in my recent blog post, here . Additionally, on my approvals page - I wanted to be able to list all of the current pending items. Then, allowing the user to filter down based upon the characteristics of the content items so they can decide which items they want to approve as makes sense to their scenario.

Those characteristics didn’t quite feel like they matched my scenario. What happens if I don’t happen to approve a pending content item in time? What happens if in the future, I want to build functionality to re-post an item of content? A queue based approach didn’t quite fully match up to my needs. Instead, it felt like I needed a lightweight storage mechanism. Especially due to the fact that I wanted to query over all current pending items. There are workarounds that you can use to achieve this using a queue. However, all of these considerations started adding up. This led me to Azure Table Storage. I could have of course chose Azure Cosmos DB or similar, but I do not have any high availability / geo-redundancy requirements. Instead, I want to keep this design as cost optimized as possible for the time being.

Once an item of content has been detected, it’s stored in Azure Table Storage. All content items use the same partition key, and a GUID is generated to ensure each item is unique within the table.

Exploring the Approval Phase

The approval phase is the aspect which required the most engineering effort. In the previous iteration of the architecture, I chose Power Automate for this scenario. Logic Apps does have capability for approvals, but it isn’t quite at the same level as Power Automate.

For my requirements, not only did I need an option for Approve / Deny, but I also needed an option to add a comment. This comment is what would eventually be the message that is posted alongside my social media post.

As I worked through the initial architecture, it quickly became clear that Power Automate could be replaced with something custom. Power Automate is an excellent solution. However, as I did not have access to the Azure connections (and was not willing to pay the add-on needed for these), it would be limited in it’s effectiveness.

Not only that, but I wanted to add additional functionality to the approval process. For example, associating multiple ‘Action Types’ with a Content Item (e.g. post immediately to these platforms, schedule a post to these platforms, add this to a roundup mail at the end of the month, etc.). This functionality was beyond the scope of Power Automate and what it could provide me out of the box. So, this took me back to a path that I’m very familiar with… building a Static Web App to suit my scenario!

In the past, I’ve typically used VueJS for my custom-built Single Page Applications, so it was the quick and easy choice for me to adopt this framework once again.

I had another choice. I could go ahead and use an Azure Storage Account or Azure Static Web Apps to host the Single Page Application (more on comparing those options in this Cloud With Chris blog post ). I decided to go for neither option, and build upon / be inspired by a URL Shortener service from Isaac Levin which I use as a dependency in this project. In this project, the Single Page Application is rendered in a GET Request to a specific endpoint call in the Azure Function. This allows me to use the Function Authorization key as a simple authorization mechanism, so that it’s not presenting an unprotected page to the end-user, and acts almost like an ‘admin password’ to the Approvals workflow. I know that this isn’t as rigorous as could be from an Authorization/Authentication perspective, but is on my backlog to integrate with Azure Active Directory over time. However, it serves my immediate requirements and can be iterated on in the future.

Once an item of content is approved, it is then processed by a Durable Function. A Durable Function is a concept within Azure Functions, where you can write stateful functions in a serverless compute environment.

To get the message into the Service Bus Topic, there are a number of distinct tasks that I need to complete. The pseudocode for this is as follows -

• This is where I have adopted Isaac’s Open Source project as a dependency in my own workflow. Any links that get sent to my various Social Media platforms will have a cloudchris.ws link associated with them. Along with this request, I pass the name of the platform that is being posted to, so that I can understand in my Google Analytics telemetry which channels may be the most popular and where I should focus my efforts over time.
• For example, if this blog post was detected through the integration platform, and we chose to associate 3 actions. One to immediately post to LinkedIn, Twitter and Facebook. One to schedule a post at a later point to LinkedIn, Twitter and Facebook. And finally, an immediate post to the r/Azure subreddit on reddit. This step would transform the ‘monolithic message’ into 3 separate messages.
• In the above example, the immediate and schedule posts to LinkedIn, Twitter and Facebook would each generate 3 messages. Therefore, 6 messages in total (from the two action types).
• A transformation step to ensure a consistent data format is then completed on each individual message (by my count, in the example above - there would be 7 messages being posted to the Service Bus Topic).
• Finally, the message is then sent to the Azure Service Bus Topic.

Note: If you’re interested in my Durable Functions implementation, you can take a look over on GitHub . The Azure Functions/Approvals UI Implementation is open source, and I hope over time to open source the Logic Apps configurations as well.

If a content item is rejected, it is currently removed from the Azure Table Storage mentioned in the Approval Phase. However, I’m considering changing this functionality so that I can see the history of messages posted, as well as re-post messages if desired.

As a reminder, I’m using the serverless tier of Azure Functions. If there was an issue with my Azure Function app mid-processing, I’d want the processing engine to be able to recover from downtime. This is why I’ve opted for using Durable Functions, as it will enable the Azure Function to continue processing from the last checkpoint that it had reached (i.e. the last point it externalised it’s state).

Exploring the Processing Phase

At this point, we have individual messages per content item, per action, per platform. This is deliberate and by design, to enable the processing phase to be extensible over time. If I add additional actions, or additional platforms, then this should just require tweaks to the Logic Apps in the processing phase, rather than a huge refactoring of the entire architecture like in the first iteration.

This granularity of message metadata enables the messages to be routed appropriately through the system, to the correct endpoint for further processing. But, wait a moment - How do the messages get routed?

Quite simply, actually. This is where the Topics and Subscriptions functionality of Azure Service Bus comes in. Posting to a Topic is similar to posting to a Queue, aside from the fact that there may be multiple ’listeners’ to that queue.

This is where the magic comes in. For each of those subscribers (or ’listeners’), I have a filter associated so that it only receives the messages that it is interested in (i.e. matching the filter conditions).

So in summary, the key difference between a queue and topics/subscriptions for me is that a queue has a single input, and a single output. Topics/Subscriptions have a single input, but one or more potential outputs, which enables my routing scenario.

In my design, I have a specific Logic App for each action type (e.g. immediate posting, scheduled posting, roundup posting, etc.). That means that there is a subscription for each of these action types. Each of those subscriptions has a filter associated, to ensure that the necessary messages are routed to that subscription. Each message has an actionType property, and will be set either as immediate, schedule, roundup, etc.

There is currently some duplication of functionality across these Logic App ‘Action processors’ in the processor phase (i.e. same social platforms across each of these, which looks suspiciously similar in each implementation currently). As I implement additional actions, I’ll look to identify whether the logic is exactly the same (and can therefore be consolidated), or whether this implementation should remain independent.

That, in a nutshell, is my evolved Cloud With Chris integration architecture. That is how I post so frequently on social media, with my own content/narrative. I can assure you that I do get work done during the day, and am not constantly posting on social media in real-time! This saves me a lot of time, allowing me to ‘batch review’ the content for approval/rejection. During the approval step, I can associate the narrative that I’d like to post with that content on a per social-platform basis, and per action basis.

This has given me some significant flexibility and functionality above the previous solution, while reducing the technical debt that was in place and standardising on a set of technologies.

So what do you think? How would you approach this differently? Something I’ve missed, or could consider as part of my ongoing evolution to this integration platform? I’d love to hear from you, get in touch over on Twitter, @reddobowen .

So that’s it for this post. Until the next one, thanks for reading - and bye for now!

Azure Storage Queues vs Azure Service Bus Queues - Which should I use when?

I’ve recently been involved in a few integration focused discussions, where there is a requirement to bring together multiple separate systems. If you’ve been following the Architecting for the Cloud, one pattern at a time series, then you’ll have heard Peter Piper repeat a common phrase - ‘High Cohesion, Low Coupling’.

26 - The Pub Sub, Priority Queue and Pipes and Filter Patterns

Ever wondered how complex integration systems / enterprise messaging works? Curious about whether there’s a way to bypass and prioritise certain messages? Then join this episode as Chris Reddington and Will Eastbury explore the Priority Queue and Pipes and Filter patterns. Spoiler - These aren’t new cloud design patterns, and are well known patterns in the messaging world!

21 - The Queue Based Load Levelling and Competing Consumers Pattern

Do you have an application with some specific requirements around scalability, and continuity of service? What happens if your service is hit by heavy load? Could performance/reliability issues cause an impact to your solution? This is where both the queue-based load levelling and competing consumers patterns shine. Tune in and listen to Chris speak with Will Eastbury as they discuss both of these patterns. This is another episode in the series of Architecting for the Cloud, one pattern at a time.

How to Integrate Microsoft Azure Into PowerPoint

Microsoft Azure and PowerPoint are two powerful tools that can be seamlessly integrated to enhance the functionality and effectiveness of presentations. By combining the cloud computing capabilities of Azure with the presentation capabilities of PowerPoint, users can unlock a wide range of possibilities, from leveraging artificial intelligence services to integrating real-time data. In this comprehensive guide, we will explore the benefits of using Microsoft Azure in PowerPoint presentations and provide step-by-step instructions on how to integrate Azure into your PowerPoint workflow.

Table of Contents

Understanding Microsoft Azure and its Benefits

Microsoft Azure is a cloud computing platform that offers a wide range of services and tools to help businesses and individuals build, deploy, and manage applications and services. With Azure, users can leverage scalable computing power, storage resources, and advanced analytics capabilities to drive innovation and accelerate their workflows.

When it comes to PowerPoint presentations, integrating Microsoft Azure opens up new possibilities for enhancing the visual appeal, interactivity, and analytical capabilities of slides. By leveraging Azure services such as Cognitive Services, Machine Learning, and IoT integration, users can take their presentations to the next level and deliver more impactful messages.

One of the key benefits of using Microsoft Azure for PowerPoint presentations is the ability to access and utilize a vast library of pre-built templates and design elements. Azure provides a wide range of professionally designed templates that can be easily customized to suit specific presentation needs. Additionally, users can take advantage of Azure’s design tools and features to create visually stunning slides with ease.

Exploring the Power of PowerPoint

Before diving into the process of integrating Microsoft Azure into PowerPoint, it’s important to understand the power of PowerPoint as a presentation tool. PowerPoint allows users to create visually appealing slides, add multimedia elements, and deliver engaging presentations. With its intuitive interface and a wide range of features, PowerPoint has become the go-to choice for businesses, educators, and individuals looking to communicate their ideas effectively.

One of the key advantages of PowerPoint is its ability to enhance the visual impact of presentations. Users can choose from a variety of pre-designed templates or create their own custom designs to suit their specific needs. Additionally, PowerPoint offers a wide range of formatting options, allowing users to easily customize the appearance of their slides with colors, fonts, and graphics.

Another powerful feature of PowerPoint is its ability to incorporate multimedia elements. Users can easily insert images, videos, and audio files into their slides, making presentations more dynamic and engaging. This allows presenters to effectively convey their message and capture the attention of their audience.

The Importance of Integration in Modern Presentations

In today’s fast-paced world, delivering static and boring presentations is no longer enough to capture the audience’s attention. Integrating Microsoft Azure into PowerPoint brings a new level of interactivity, dynamic content, and real-time data to presentations, making them more engaging and impactful.

By integrating Azure services into PowerPoint, presenters can leverage advanced analytics, machine learning algorithms, and AI-powered features to create visually stunning slides, automate processes, and provide valuable insights to the audience. This integration opens up a wide range of possibilities for enhancing data visualization, automating tasks, and delivering personalized experiences.

Furthermore, the integration of Microsoft Azure with PowerPoint allows presenters to seamlessly collaborate with others in real-time. With cloud-based storage and sharing capabilities, multiple presenters can work on the same presentation simultaneously, making it easier to create cohesive and cohesive presentations. This collaborative feature also enables presenters to gather feedback and make revisions in real-time, ensuring that the final presentation is polished and well-rounded.

Step-by-Step Guide to Integrating Microsoft Azure into PowerPoint

To start integrating Microsoft Azure into PowerPoint, you’ll need to have both Azure and PowerPoint installed on your computer. Once you have these tools in place, follow the step-by-step guide below:

• Open PowerPoint and create a new presentation or open an existing one.
• Click on the “Insert” tab in the PowerPoint Ribbon and select “Azure” from the menu.
• Authenticate your Azure account by providing your credentials.
• Explore the available Azure services and select the ones that best fit your presentation needs.
• Customize the Azure services by adjusting settings, data sources, and parameters.
• Insert the Azure service into your presentation by dragging and dropping it onto the desired slide.
• Configure the Azure service to display the desired data or functionality.
• Repeat the above steps for each Azure service you want to integrate into your presentation.
• Save your presentation and test the integration by running the slideshow.

Preparing Your PowerPoint Presentation for Azure Integration

Before integrating Microsoft Azure services into your PowerPoint presentation, it’s important to plan and organize your content. Determine which slides will benefit from Azure integration and identify the specific data or functionality you want to incorporate.

Additionally, make sure you have a clear understanding of the Azure services you plan to use and how they can enhance your presentation. Familiarize yourself with the documentation and guides provided by Microsoft to ensure a smooth integration process.

Choosing the Right Azure Services for Your Presentation Needs

Microsoft Azure offers a wide range of services that can be integrated into PowerPoint presentations. When selecting the appropriate Azure services, consider the specific goals and objectives of your presentation.

For example, if you want to enhance data visualization, consider leveraging Azure Cognitive Services, which provide AI-powered image recognition and natural language processing capabilities. If you want to incorporate real-time data, explore Azure IoT integration options. By choosing the right Azure services, you can tailor your presentation to meet the needs and expectations of your audience.

Setting Up Your Azure Account and Accessing PowerPoint Integration Tools

To integrate Microsoft Azure into PowerPoint, you’ll need to have an Azure account. If you don’t have one already, you can sign up for a free trial or a paid subscription on the Azure website.

Once you have an Azure account, you can access the PowerPoint integration tools by installing the Azure add-in for PowerPoint. This add-in provides a seamless integration experience and allows you to browse and insert Azure services directly into your presentation.

Leveraging Azure Cognitive Services in PowerPoint Presentations

Azure Cognitive Services offer a range of AI-powered features that can be integrated into PowerPoint presentations. These services include computer vision, speech recognition, natural language understanding, and more.

By leveraging Azure Cognitive Services, presenters can enhance their slides with features such as automated image tagging, voice-controlled presentations, and live language translation. These capabilities can help capture the audience’s attention and deliver a more personalized and interactive presentation experience.

Enhancing Data Visualization with Azure Machine Learning in PowerPoint

Azure Machine Learning allows users to build, deploy, and manage machine learning models at scale. By integrating Azure Machine Learning into PowerPoint, presenters can visualize data in new and interactive ways.

Through Azure Machine Learning, presenters can create dynamic charts, graphs, and data visualizations that can be updated in real-time. This integration enables presenters to convey complex ideas and insights effectively, making data-driven presentations more compelling and engaging.

Integrating Real-time Data from Azure IoT into PowerPoint Slides

Azure IoT enables the integration of real-time data from various devices and sensors. By integrating Azure IoT into PowerPoint, presenters can showcase live data and demonstrate real-time insights.

For example, if you are delivering a presentation on energy consumption, you can use Azure IoT to display real-time data from smart meters and sensors directly in your slides. This integration adds a dynamic element to the presentation, enabling the audience to see the impact of data in real-time.

Exploring the Possibilities of Azure AI in PowerPoint Presentations

Azure AI brings powerful artificial intelligence capabilities to PowerPoint presentations. By integrating Azure AI into PowerPoint, presenters can automate repetitive tasks, generate personalized content, and deliver tailored recommendations.

For instance, presenters can use Azure AI to automate the generation of slide layouts based on the content they provide. This saves time and ensures consistent design across slides. Additionally, Azure AI can analyze audience feedback in real-time and provide recommendations for improving the delivery and impact of the presentation.

Incorporating Azure Storage Solutions into PowerPoint for Seamless File Management

Azure Storage offers scalable and durable cloud storage solutions that can be integrated into PowerPoint for seamless file management. By leveraging Azure Storage, presenters can easily upload, access, and share files within their PowerPoint presentations.

Whether it’s images, videos, or other multimedia elements, Azure Storage provides a reliable and secure way to store and retrieve files for your presentations. This integration ensures that your content is easily accessible and always up to date.

Enhancing Collaboration with Azure DevOps in PowerPoint Presentations

Azure DevOps is a set of development tools and services that enable collaboration and project management. By integrating Azure DevOps into PowerPoint, presenters can showcase the progress of their projects and provide updates in real-time.

For instance, presenters can embed project boards, task lists, and timelines directly into their PowerPoint slides to provide visibility and transparency into the project’s status. This integration fosters collaboration and enables presenters to deliver impactful presentations that keep the audience informed and engaged.

Troubleshooting Common Issues when Integrating Microsoft Azure into PowerPoint

While integrating Microsoft Azure into PowerPoint can greatly enhance your presentation capabilities, there may be some common issues that you may encounter during the process. Here are a few troubleshooting tips to help you resolve these issues:

• Ensure you have a stable and reliable internet connection to access Azure services.
• Verify that you have the necessary permissions and credentials to access Azure services from PowerPoint.
• Check for any software updates for both Azure and PowerPoint to ensure compatibility.
• Refer to the Microsoft documentation or community forums for specific issues and resolutions.

Tips and Best Practices for a Successful Integration of Microsoft Azure into PowerPoint

Here are some tips and best practices to ensure a successful integration of Microsoft Azure into PowerPoint:

• Plan your presentation carefully and identify the specific Azure services that will enhance your content.
• Test the integration thoroughly before delivering the presentation to ensure everything functions as expected.
• Keep your audience in mind and use Azure integration to deliver personalized and relevant experiences.
• Stay updated with the latest features and updates of Azure and PowerPoint to leverage all the capabilities available.
• Collaborate with other stakeholders, such as data analysts or developers, to fully leverage the power of Azure integration in your presentations.

With these insights and instructions, you are now equipped to integrate Microsoft Azure into your PowerPoint presentations. By leveraging the cloud computing power, advanced analytics capabilities, and AI-driven features of Azure, you can create dynamic and impactful presentations that captivate your audience and deliver your message with greater precision and effectiveness. Start exploring the possibilities of Azure integration in PowerPoint today and elevate your presentations to new heights!

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Dynamically Copy Data from an On-Premises SQL Server to Azure SQL Database

By: Temidayo Omoniyi   |   Updated: 2024-03-14   |   Comments (2)   |   Related: 1 | 2 | 3 | 4 | > Azure SQL Database

Organizations worldwide are beginning to adopt cloud services. As a result, the painstaking task of moving data from on-premises to the cloud has been on the rise for most data professionals. Using the best platform and technique for moving data is more crucial than ever.

Azure Data Factory (ADF) is an ETL tool used by data professionals worldwide for data ingestion and transformation. It has built-in links that allow users to connect to an on-premises data source using Microsoft Integration Runtime , a third-party tool.

Project Architecture

For this article, we will dynamically copy data from an on-premises SQL Server to Azure SQL Database using the Lookup and ForEach activity. Also, we will need to use the Microsoft Integration Runtime, which will link the Azure Data Factory Source Dataset and our on-premises server.

What is Microsoft Integration Runtime?

The Microsoft Integration Runtime (IR) serves as a data integration connector, connecting to and transferring data between on-premises and cloud data stores. Since it is a managed service, infrastructure must be installed or maintained.

Since the data that will be our source is an on-premises data source on SQL Server, the IR will serve as a connection point between the on-premises and ADF.

Set Up Microsoft Integration Runtime

Before we start any data migration, we need to set an integration runtime. The self-hosted integration runtime is installed and maintained on your infrastructure, allowing you complete control over the hardware, software, and network setup of the integration runtime.

Step 1: Download and Install Integration Runtime

You must download the Microsoft Integration Runtime on your desktop computer, open your browser, and follow this URL .

In the Integration Runtime website, select the version suitable for your system and click Download .

If installed successfully, you will see the window appear below. Click Finish .

Step 2: Create IR Connection in ADF

After installing the IR, go to the ADF resource and create a new Integration Runtime connection.

In your ADF environment, click the Managed tab in the left pan and select Integration runtimes . This should open another window. Click New , and then select Azure, Self-Hosted . Click Continue .

In the new window, select Self-Hosted and click Continue .

We are expected to create a unique name for the Integration runtime setup. Click Create .

Step 3: Authentication Keys

At this stage, we need to set an authentication key in the installed Integration Runtime in our on-premises environment. Start by copying the keys provided in the Integration Runtime set-up. Now head to your installed integration runtime on your on-premises and paste the copied key.

In your IR on-premises environment, paste the copied keys and click on Register . This should take you to another environment.

In the new window, click Finish . Configuring the IR in your on-premises environment should take a couple of minutes.

Click Launch Configuration Manager to fully set up the Integration Runtime with the appropriate number of nodes.

After launching the configuration, you should get the image below showing your IR is working.

Create a Simple Copy Activity in ADF

To do this, we will create a simple copy activity to help move data from the SQL Server Management Services (SSMS) on-premises table to the Azure SQL Database.

For this demonstration, we will use Microsoft's AdventureWorks dataset and move the Human Resource Department table from on-premises to the Azure SQL cloud.

Linked Service Connection

We need to set up a linked service that will be required to connect to external data sources.

The following steps should be followed while setting up your SQL server-linked service.

Step 1: Create Server Linked Service. Click the Manager icon in your ADF. Under Linked Services, select New . Search for and click on SQL Server. Click Continue .

Step 2: Configure Linked Service Connection. To get all the requirements, first go to your SSMS and select the database you want to use. In the database, right-click and select Properties. This will open a window providing you with all the necessary information.

In ADF linked service, fill in the following configuration:

• Name: Provided your linked service with a name you can easily identify.
• Connect via Integration Runtime: Select the Integration runtime created earlier.
• Server name: This should be the server name of the SSMS.
• Authentication Type: The options are SQL authentication or Windows authentication. For this article, we will use Windows Authentication.
• Username and Password: To find your PC username, open a command prompt and type whoami .
• Test Connection and Create: Click on the test connection and Create .

Create Source Dataset

In ADF, click on Data and create new data. In the new window, search for SQL Server and continue.

• Set Properties. Set the following properties to fully connect with SSMS.
• Name: Give your Dataset source a name that can easily be identified.
• Linked service: Select the linked service created earlier.
• Integration Runtime: Select the Integration Runtime created earlier.
• Table Name : Select the list from the dropdown of the table you want to work with.

Create Sink Linked Connection

The sink will be our Azure SQL Database since we plan to move the data from on-premises SSMS to Azure SQL Database. For information on creating an Azure SQL Database, see this previous article: Data Transformation and Migration Using Azure Data Factory and Azure Databricks .

The following configuration should be provided to set the sink configuration:

• Name: Provide the name of the linked service connection you want to use.
• Connection via integration runtime: Select the default integration runtime in your ADF.
• Server name: This is the server name of your Azure SQL Database.
• Authentication: Settle the authentication type. For this article, we will use SQL authentication for the sink.
• Username and Password: This is the username and password needed to log in to your Azure SQL Database.

Create Sink Dataset

We need to create a dataset in ADF that will serve as our sink data. Before we do that, let's create data tables in our Azure SQL Database.

Get Datatype. We plan to move the data from HumanResources.Department table from SSMS to Azure SQL Database. But first, let's get the exact datatype we will use in our Azure SQL Database when creating the table.

The following SQL command will help get the exact datatype for HumanResources.Department table from SSMS:

Now that we know the datatype for the individual columns/fields, let's go to Azure SQL Database. I will be using Azure Data Studio as it provides me with an easy user interface.

Now that the table is in our Sink Azure SQL Database, we need to create the sink dataset in our ADF.

In ADF, click Create New Dataset and select Azure SQL Database. Fill in the following configuration settings:

Copy Activity

To set the copy activity, you will need the source and sink datasets created earlier. From the activity tab, drag the copy activity to the pipeline canvas, set the source and sink, and then click Debug to run the pipeline.

The output shows that data moved successfully from SSMS on-premises to the Azure SQL Database.

Using Azure Data Studio, the table output can be seen as shown below.

Set ADF Copy Parameter

In the ADF pipeline, parameters dynamically pass values to ADF activities or datasets.

Set Parameter Pipeline

We must add parameters to all the necessary components in our pipeline, including the source, sink, and activities .

Step 1: Set Source Parameter. In ADF, click on the source dataset in the left pane and select the Parameters tab. In the parameter field, add a new parameter called " SourceRelative ."

After you have created the parameter, select the Connection tab. We want to create a Dynamic file path setting. In the Connection tab, select Add dynamic content . Note: Before you add the dynamic content, enable the editing feature by clicking the Edit check box. This should open another window where you will set the parameter we just created.

In the Dynamic Content Pipeline builder, select the parameter we just created. Click OK .

Step 2: Set Sink Parameter. Repeat the same process for the sink dataset. Start by clicking on the sink dataset and selecting the Parameters tab. In the Parameters tab, click on New and add " Filename ."

Next, we need to add the new parameter Filename to the Connection . Click the Connection tab, select Add dynamic content, and fill in the information below.

Step 3: Set Pipeline Parameter. We need to set the parameter for the ADF pipeline we created earlier. Select the pipeline, select the Parameters tab, click New, and fill in the following information, as seen in the image below.

Step 4: Set Activity Parameter. We also need to set parameters for the copy activity we created. This will be done on both the source and sink tabs.

Start by clicking on the Source tab of your copy activity and selecting Dynamic Content . This should take you to another window where you can choose the source parameter. Click OK .

Notice that the value in the Dynamic Content now shows the parameter name.

Repeat the same process for the Sink tab.

Step 5: Publish All, Validate, and Debug. This is the last step. We need to Publish all to save all the changes made in our ADF environment. Validate helps check for errors, and Debug is a manual way of running the pipeline.

Note : Before debugging, remove all records in the table we created earlier using the TRUNCATE command in SQL.

After running your SQL SELECT statement, all the values or records in the table are empty.

After you click to debug the table, a new window will appear on the right side. Complete the following details. The Filename should be from your Azure SQL Database table, and SourceRelative should be the name of the table from SSMS. Click OK .

After successfully running the pipeline, you will receive a success message.

Dynamically Copy Multiple Tables from the SQL Server to the Azure SQL Database

The essence of this article is to help readers learn how to move data in bulk from an on-premises SQL Server to an Azure SQL Database. To get started, we must create the tables in our Azure SQL Table for the other HumanResources.

Note: Each table in Azure SQL Database should have the same datatypes for each field/column.

Get Tables Datatypes

This process will be done for all the HumanResources Tables in the AdventureWorks database. We can create the appropriate tables in our Azure SQL Database with the right datatypes.

File Configuration

Now that we have created the needed tables in Azure SQL Database, we need to create a JSON file to help us dynamically pick the different tables from our on-premises SSMS and migrate them to Azure Database.

Lookup Activity

This activity in ADF retrieves data or information from a dataset and feeds the received information to subsequent activities in a pipeline.

ForEach Activity

This activity is used to iterate over a collection of items and perform a specific action on them.

The following steps are needed to perform Dynamic Migration from SSMS to Azure SQL Database:

Step 1: Upload Config File to Storage. The first step is to upload the JSON file to our Azure Data Lake storage. The image below shows the uploaded config file.

Create a new dataset in ADF and set the file path directory to pick the data from the Azure storage. Also, take into consideration that this is a JSON file type.

Step 2: Add Lookup Activity. In your ADF environment, search for Lookup and add it to the pipeline canvas.

Select the Lookup activity and perform the following configuration in the Settings tab.

• Source dataset: This is the config dataset in JSON we just added from the Data Lake. It contains information about the directory from both source and sink.
• First row only: Uncheck the box to allow the data to read the entire file in the Data Lake JSON file.

Step 3: Add ForEach Activity. Search for the ForEach activity and drag it to the pipeline canvas. In your design canvas, connect the Lookup activity to the ForEach activity.

After connecting the Lookup activity to the ForEach activity, we need to configure the ForEach. In ForEach , click the Settings tab. Click on the dynamic content in the items area.

A new window will open. Select the activity output of the Lookup and add .value to the end of the code. Click OK .

After clicking OK, you will notice the Lookup activity has been added to the item's dynamic content area.

Step 4: Cut and Paste the Copy Activity in the ForEach. We need to make the Copy Activity dynamic by cutting it from our pipeline design canvas and pasting it inside the ForEach activity . You can see the image below on how we can go about this.

Now, go back to the pipeline. Click on the pipeline canvas, select the Parameters tab, and delete all the parameters.

Add Source Copy Activity. Deleting the Source and Sink parameter from the pipeline canvas affected the source and sink in the copy activity. Now, go to the ForEach and select the Copy activity inside.

You will receive a warning indication from both the Source and Sink tab. Let's start by fixing the SourceRelative path.

Delete the value and select the dynamic content.

After deleting the pipeline parameter, click in the Dynamic Content, select the ForEach Loop Config , and add the SourceRelative from the JSON file to the item line of code.

Add Sink Copy Activity. Repeat the same process for the Sink dataset in the Copy activity .

Notice that the dynamic content for the Filename has changed.

Step 5: Publish All, Validate, and Debug. Publish All to save all changes, Validate to check for errors, and Debug to manually run your pipeline. If all is done correctly, you should not have errors.

As you can observe in the Pipeline Output, all data flows ran successfully and copied data from the SQL Server on-premises to the Azure SQL Database tables.

Let's go to our Azure Data Studio or Azure Portal for SQL Database. Run the following SQL command to confirm that the data successfully migrated to the Azure Database table:

In this article, we have learned how to dynamically move large amounts of data tables from SQL Server on-premises to Azure SQL Database tables. We also discussed Microsoft Integration runtime, which provides a means of connectivity from ADF to on-premises and its installation. In our next tip, we will migrate a SQL Server database to an Azure SQL Database.

• Tutorial: Migrate SQL Server to Azure SQL Database using DMS (classic)
• Migration guide: SQL Server to Azure SQL Database
• Tutorial: Migrate SQL Server to Azure SQL Database (offline)

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Announcements , Azure Logic Apps , Compute , Integration

Announcing Azure Integration Service Environment for Logic Apps

By Kevin Lam Principal Program Manager, Azure Logic Apps

Posted on February 26, 2019 2 min read

• Tag: Virtual Machines

A new way to integrate with resources in your virtual network

We strive with every service to provide experiences that significantly improve the development experience. We’re always looking for common pain points that everybody building software in the cloud deals with. And once we find those pain points, we build best-of-class software to address the need.

In critical business scenarios, you need to have the confidence that your data is flowing between all the moving parts. The core Logic Apps offering is a great, multi-faceted service for integrating between data sources and services, but sometimes it is necessary to have dedicated service to ensure that your integration processes are as performant as can be. That’s why we developed the Integration Service Environment (ISE), a fully isolated integration environment.

What is an Integration Service Environment?

An Integration Service Environment is a fully isolated and dedicated environment for all enterprise-scale integration needs. When you create a new Integration Service Environment, it is injected into your Azure virtual network, which allows you to deploy Logic Apps as a service on your VNET.

• Direct, secure access to your virtual network resources. Enables Logic Apps to have secure, direct access to private resources, such as virtual machines, servers, and other services in your virtual network including Azure services with service endpoints and on-premises resources via an Express Route or site to site VPN.
• Consistent, highly reliable performance. Eliminates the noisy neighbor issue, removing fear of intermittent slowdowns that can impact business critical processes with a dedicated runtime where only your Logic Apps execute in.
• Isolated, private storage. Sensitive data subject to regulation is kept private and secure, opening new integration opportunities.
• Predicable pricing. Provides a fixed monthly cost for Logic Apps. Each Integration Service Environment includes the free usage of 1 Standard Integration Account and 1 Enterprise connector. If your Logic Apps action execution count exceeds 50 million action executions per month, the Integration Service Environment could provide better value.

Integration Service Environments are available in every region that Logic Apps is currently available in, with the exception of the following locations:

• West Central US
• Brazil South
• Canada East

Logic Apps is great for customers who require a highly reliable, private integration service for all their data and services. You can try the public preview by signing up for an Azure account . If you’re an existing customer, you can find out how to get started by visiting our documentation, “ Connect to Azure virtual networks from Azure Logic Apps by using an integration service environment .”

Let us know what you think of Azure and what you would like to see in the future.

Provide feedback

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Azure Integration Environments documentation (preview)

Centrally organize Azure resources for integration solutions. Model business processes that map to Azure resources. Collect and track key business data.

About Azure Integration Environments

• What is Azure Integration Environments?

How-To Guide

• Create an integration environment
• Create an application group
• Create a business process
• Map business process to workflow
• Deploy business process and tracking
• Manage a business process