The Pain: When Scaling Up Isn’t Enough
As software developers, we’ve all been there: You’ve deployed your web app to Azure, tuned the App Service Plan, scaled up to a beefier tier, and yet… performance still lags.
In our case, the bottleneck was painfully clear: long-running processes like importing or exporting thousands of records.
Even after scaling up, we faced:
Slow response times for end users
Timeout errors during large data exports/imports
Unreliable performance under heavy load
The truth? Scaling up only gave us more horsepower—it didn’t change the fact that our web app was still doing all the heavy lifting synchronously.
The Insight: Decouple and Go Serverless
Instead of throwing more compute at the problem, we needed to redesign the architecture to handle heavy workloads asynchronously.
That’s where Azure’s serverless ecosystem comes in. By introducing queues, functions, and event-driven processing, we can offload long-running tasks from the web app, improve responsiveness, and make the system more resilient.
The Proposed Architecture
Here’s the high-level design:
1. Frontend Web App (Azure App Service / Azure Static Web Apps)
Handles user requests instantly without waiting for the heavy process to finish.
When a user triggers a large export/import, the app sends a job request to a queue instead of processing it directly.
2. Azure Storage Queue / Azure Service Bus
Acts as a buffer between the web app and the processing layer.
Ensures durability of requests and smooth handling of spikes in demand.
3. Azure Functions (Serverless Workers)
Triggered automatically when a new message arrives in the queue.
Executes the heavy data processing in the background.
Can scale horizontally to handle multiple jobs in parallel.
4. Azure Blob Storage / Azure SQL Database
Stores processed data or generated files.
Optimized for bulk operations and parallel writes.
5. Notification Layer (SignalR / Email / Webhook)
Notifies the user when the process is complete.
Allows the user to download results or view status updates.
Workflow Example: Exporting Thousands of Records
User clicks “Export” in the web app.
Web app creates a job message with parameters (date range, filters, format) and pushes it to Azure Storage Queue.
Azure Function picks up the message, queries the database in batches, and writes the export file to Azure Blob Storage.
Once done, the function updates job status in the database and sends a SignalR notification to the user.
User downloads the file from a secure link.
Why This Works
No more timeouts – The web app responds instantly after queuing the job.
Better reliability – Queues ensure no job is lost, even if a worker fails.
Elastic scaling – Azure Functions scale automatically with demand.
Cost efficiency – Pay only for the compute time you use.
User-friendly – Users can continue working while heavy tasks run in the background.
Additional Enhancements
Azure Durable Functions for orchestrating multi-step workflows.
Retry policies for transient failures.
Application Insights for monitoring performance and failures.
Role-based access control (RBAC) to secure data exports/imports.
Conclusion
Scaling up is not always the answer. By rethinking architecture and embracing serverless, event-driven patterns, we can build systems that are fast, reliable, and cost-effective—even under heavy workloads.
If you’re struggling with long-running processes in Azure, consider decoupling your workloads with queues and serverless functions.
Why Cloud-Native is the Future and How to Get Started with Visual Studio
The world of software development is constantly evolving, and one of the most significant shifts we're seeing today is the move towards cloud-native applications. But what exactly does "cloud-native" mean, and why should you care? If you're a developer, especially one working with Visual Studio, understanding and embracing cloud-native principles is crucial for staying relevant and building robust, scalable, and resilient applications.
What is Cloud-Native?
At its core, cloud-native is an approach to building and running applications that leverages the benefits of cloud computing. It's not just about running your existing applications in the cloud; it's about designing and developing new applications specifically for the cloud. This often involves:
Microservices: Breaking down large, monolithic applications into smaller, independently deployable services that communicate with each other. This allows for greater agility and easier maintenance.
Containers: Packaging applications and their dependencies into lightweight, portable units (like Docker containers). This ensures consistent environments from development to production.
Orchestration: Automating the deployment, scaling, and management of containers, typically using platforms like Kubernetes.
DevOps: Fostering a culture of collaboration and automation between development and operations teams, leading to faster release cycles and improved quality.
Continuous Delivery: Automating the entire software release process, from code commit to production deployment.
Imagine your application as a bustling city. In a traditional monolithic approach, it's one giant building housing everything. With cloud-native and microservices, it's a collection of specialized, smaller buildings, each with its own purpose, and all working together harmoniously. And the good news is, you can move your building between cloud provider.
Why is Learning Cloud-Native Essential?
The shift to cloud-native isn't just a trend; it's becoming the standard for modern application development. Here's why you need to get on board:
Scalability: Cloud-native applications are designed to scale effortlessly. Need to handle a sudden surge in traffic? Cloud-native platforms can automatically spin up more instances of your services.
Resilience: If one microservice fails, the rest of your application can continue to function. This makes your applications more robust and less prone to complete outages.
Agility and Speed: Microservices allow teams to work independently and deploy updates more frequently. This accelerates development cycles and time-to-market.
Cost-Effectiveness: By optimizing resource utilization and scaling only what's needed, cloud-native can lead to significant cost savings compared to traditional approaches.
Innovation: Cloud-native development encourages the use of cutting-edge technologies and services offered by cloud providers, enabling you to build more innovative features.
Cloud-Native Tools and Visual Studio: A Perfect Match
For developers working with Visual Studio, embracing cloud-native doesn't mean abandoning your familiar environment. In fact, Visual Studio has become an incredibly powerful tool for building, debugging, and deploying cloud-native applications. Here's how it helps you work with key cloud-native tools:
1. Docker: Containerizing Your Applications
Docker is the de facto standard for containerization. Visual Studio provides excellent integration with Docker, allowing you to:
Add Docker Support: Easily add Dockerfile and Docker Compose files to your projects directly from Visual Studio.
Build and Run Containers: Build Docker images and run your applications within containers without leaving the IDE.
Debug in Containers: Debug your application directly within a running Docker container, providing a faithful representation of your production environment. This is a game-changer for troubleshooting container-specific issues.
Here's how Visual Studio streamlines Docker integration:
2. Kubernetes: Orchestrating Your Containers
Kubernetes is the leading platform for automating the deployment, scaling, and management of containerized applications. While Kubernetes itself runs on a cluster, Visual Studio helps you prepare your applications for Kubernetes deployment:
Helm Charts: Visual Studio can assist in generating and working with Helm charts, which are packages for Kubernetes applications.
Deployment to Azure Kubernetes Service (AKS): With the Azure extensions for Visual Studio, you can directly publish your containerized applications to AKS, Microsoft's managed Kubernetes service.
Local Kubernetes Development (via tools like Minikube/Docker Desktop Kubernetes): While not direct Visual Studio integration, you can use Visual Studio to develop and debug your applications locally while they are configured to run against a local Kubernetes instance, mimicking a production environment.
3. Azure DevOps: Implementing Continuous Delivery
Azure DevOps provides a comprehensive set of services for implementing DevOps practices, including Continuous Integration (CI) and Continuous Delivery (CD). Visual Studio integrates seamlessly with Azure DevOps, enabling you to:
Source Control: Manage your code in Azure Repos (Git) directly from Visual Studio.
Build Pipelines: Trigger automated builds and tests of your cloud-native applications whenever code is pushed.
Release Pipelines: Automate the deployment of your containerized applications to various environments, including Kubernetes clusters.
Getting Started with Cloud-Native in Visual Studio
Ready to dive into cloud-native development with Visual Studio? Here's a simplified roadmap:
Install Docker Desktop: This provides a local Docker environment and often includes a single-node Kubernetes cluster.
Enable Docker Support in Visual Studio: Right-click on your project in Solution Explorer and choose "Add" > "Docker Support."
Explore Microservices: Start by building a simple application with two or three microservices to understand the communication patterns.
Learn Kubernetes Basics: Familiarize yourself with core Kubernetes concepts like Pods, Deployments, and Services.
Utilize Azure Extensions: If targeting Azure, install the relevant Visual Studio extensions for seamless deployment to AKS.
The journey into cloud-native is an exciting one, opening up new possibilities for building resilient, scalable, and high-performing applications. With the robust tooling and integrations offered by Visual Studio, you're well-equipped to embark on this journey and shape the future of software development. It is a good time to learn cloud native right know, Create cloud native apps with Azure and open-source software - Training | Microsoft Learn
The investment for more than IDR 27 trillion, with plan to create significant economic growth and employment opportunities is the idea of Azure in Indonesia! Compared with AWS that launched in December 2021, Azure is too late in the Indonesia cloud market. AWS investment with more than 71 trillion IDR shows the incremental services starting from EC2, S3, RDS, DynamoDB, Sage Maker, and of course IOT solution. While in March 2025, we only see Azure only available for Azure VM, AI, IOT, and Azure Arc. Wondering why there is no SQL Azure and App Services that mostly need in current solution ecosystem. Again, Azure is too late in the Indonesian market!
So how to make to make it better! in the late situation like this. This is some advises for Azure Lover!
integration in AI driven solution. Azure has potential in AI solution like Azure Open AI.
Targeting hybrid cloud with competitive price, make the VM cheaper than EC2 than Azure can get attention in B2B business
Integration with other Microsoft product such as Office 365 and Dynamic 365. For example, Azure Logic integration scenario or Azure AI search for enterprise is a good solution for that!
While AWS has a head start and a larger investment, Azure's entry will enhance competition and provide more options for businesses in Indonesia. is it too late? we dont know! here are the facts! for simplicity we dont compare with Google or ALI Cloud.
AWS in Indonesia
Availability Zones: AWS Jakarta Region has three Availability Zones, providing high availability and disaster recovery options.
Services: AWS offers a wide range of services, including compute (EC2), storage (S3), databases (RDS, DynamoDB), machine learning (SageMaker), and IoT solutions.
Investment: AWS has committed USD 5 billion over 15 years, emphasizing its long-term presence in Indonesia.
Focus Areas: AWS is widely used for startups, enterprises, and public sector projects, offering localized support and compliance with Indonesian regulations.
Azure in Indonesia
Indonesia Central Region: Azure Indonesia Central is launch in March-2025, with multiple Availability Zones.
Services: Azure provides services like virtual machines, Azure AI, Azure IoT, and hybrid cloud solutions with Azure Arc.
Integration: Azure is known for its seamless integration with Microsoft products like Office 365 and Dynamics 365, making it a strong choice for businesses already in the Microsoft ecosystem.
Focus Areas: Azure emphasizes AI-driven solutions, hybrid cloud capabilities, and enterprise-grade security.
Key Differences
Current Availability: AWS is already operational in Indonesia, while Azure's region is still under development.
Ecosystem: AWS has a broader range of services globally, but Azure excels in hybrid cloud and Microsoft product integration.
Investment Scale: AWS has a larger financial commitment in Indonesia compared to Azure.
When building software in C# and .NET we need to create secure codes to make sure that the software is running well. Here are some keys steps to implement secure code:
Input validation. Always validate user input to prevent attacks like SQL injection and cross-site scripting (XSS).
using System.Text.RegularExpressions;
string userInput = GetUserInput();
if (!Regex.IsMatch(userInput, @"^[a-zA-Z0-9]*$"))
{
throw new ArgumentException("Invalid input");
}
Use Parameterized Queries: When accessing a database, use parameterized queries to prevent SQL injection.
using (SqlConnection connection = new SqlConnection(connectionString))
{
string query = "SELECT * FROM Users WHERE Username = @Username";
SqlCommand command = new SqlCommand(query, connection);
command.Parameters.AddWithValue("@Username", username);
connection.Open();
SqlDataReader reader = command.ExecuteReader();
// ...
}
Secure password. Store passwords using a strong hashing algorithm like SHA-256 or bcrypt.
using System.Security.Cryptography;
using System.Text;
string password = "YourPassword";
using (SHA256 sha256Hash = SHA256.Create())
{
byte[] bytes = sha256Hash.ComputeHash(Encoding.UTF8.GetBytes(password));
StringBuilder builder = new StringBuilder();
for (int i = 0; i < bytes.Length; i++)
{
builder.Append(bytes[i].ToString("x2"));
}
string hashedPassword = builder.ToString();
}
always use HTTPS to encrypt data transmitted between the client and server. Configure your application to require HTTPS.
Authentication and Authorization. Implement robust authentication and authorization mechanisms using frameworks like ASP.NET Identity.
Handle Exceptions Securely. Avoid displaying detailed error messages to users. Log errors internally and provide generic error messages.
Regular security audits. Conduct regular security audits and code reviews to identify and fix vulnerabilities.
Keep dependencies up to date. Regularly update libraries and frameworks to patch known vulnerabilities.
secure configuration management by using tools like Terraform or Azure Resource Manager (ARM) templates to define and manage cloud resources securely.
Identity and Access Management (IAM) Use managed identities to secure access to cloud resources without hardcoding credentials.
Store sensitive information like API keys, connection strings, and certificates in secure vaults such as Azure Key Vault or AWS Secrets Manager.
Implement network security such as VNET isolation, network security group, and web application firewall
Microsoft offers a variety of technologies and platforms for developing generative AI solutions, enabling organizations to harness the power of AI to create innovative applications and services. There are four technologies that can be adopted by the developer
Azure Open AI Services provides access to advanced AI models, including GPT-4, through easy-to-use APIs. This democratizes AI development by allowing developers of any skill level to integrate AI functionality into their applications without needing specialized knowledge or hardware investments
Azure AI Studio is a unified environment for building, training, and deploying AI models. It offers tools for data preparation, model training, and deployment, making it easier for developers to create and manage AI solutions.
The Copilot Ecosystem includes tools and services that enhance productivity and creativity by leveraging generative AI. It provides capabilities for generating ideas, designing personalized marketing campaigns, and more
The Azure AI Model Catalog offers a collection of pre-trained models that developers can use to jump-start their AI projects. These models cover a wide range of tasks, from language translation to image recognition, providing a solid foundation for building custom solutions.
You can use Visual Studio and Azure By leveraging these technologies and platforms, developers can create powerful generative AI solutions that drive innovation and efficiency across various industries. You can learn more about Generative AI here
Auto-scaling is a feature that dynamically adjusts the amount of compute and storage resources allocated to your Azure SQL Database based on current workloads. This capability ensures that your database can handle varying loads efficiently, providing better performance and cost management. Auto-scaling in Azure SQL Database is not a native feature, but it can be implemented using Azure's powerful automation and scaling capabilities. The process involves monitoring your database's performance metrics and setting up rules that trigger scaling actions when certain thresholds are reached.
Setting Up Auto-Scaling
To set up auto-scaling for your Azure SQL Database, you'll need to follow these general steps:
Utilize Azure's monitoring tools to keep an eye on your database's performance. Pay special attention to metrics like CPU usage, memory consumption, and I/O rates.
Azure Automation allows you to create runbooks, which are collections of scripts that automate cloud management tasks. For auto-scaling, you'll write a PowerShell script that changes the performance level of your database when specific conditions are met.
Decide on the conditions that will trigger a scale-up or scale-down. For example, you might want to scale up when CPU usage exceeds 80% for a sustained period.
Azure Logic Apps can be used to schedule and orchestrate the scaling actions based on the rules you've defined. They can call the runbooks you've created in Azure Automation to perform the scaling.
Before relying on auto-scaling in a production environment, thoroughly test your setup to ensure it behaves as expected. Monitor the results and refine your scripts and rules as necessary.
Considerations and Best Practices
Azure SQL Database offers different performance tiers and service levels. Ensure that your auto-scaling setup considers the capabilities and limits of your chosen tier.
While auto-scaling can help manage costs by reducing resources during low demand, it's important to monitor your spending to avoid unexpected charges.
Consider how auto-scaling interacts with your database's failover and high availability setup. Ensure that scaling actions do not compromise the resilience of your system.
For certain scenarios, Azure SQL Database's serverless tier can automatically scale compute resources, providing a simpler alternative to custom auto-scaling solutions.
Conclusion
Auto-scaling is a powerful technique to optimize the performance and cost of your Azure SQL Database. By leveraging Azure Automation, Logic Apps, and careful monitoring, you can create a responsive and efficient database environment that scales with your needs. For detailed instructions and script examples, refer to the comprehensive tutorial provided by the Microsoft Community Hub, and explore other resources that offer insights into auto-scaling strategies and best practices. Remember, while auto-scaling can greatly enhance your database's efficiency, it requires careful planning and continuous monitoring to ensure optimal results.
.NET Development and AI: Harnessing the Power of C# and Microsoft's AI Platform
The intersection of .NET development and artificial intelligence (AI) has become an increasingly significant area of focus for software engineers and researchers alike. With the rise of advanced AI technologies. One of the trends that we have witnessed in recent years is the seamless integration of C# programming language and Microsoft's AI platform, enabling developers to create innovative software solutions that leverage the power of both.
Software developer today will have several scenarios that can be used to create software that has built in AI. Some of the scenario that commons are:
Adding conversation between your application and AI. For example, you put an AI rewrite feature in the application or have a dialog between your application and AI. On this scenario, we can use Azure OpenAI
Adding predictive maintenance capabilities to your application to forecast when equipment might fail, or sales will rise. On this scenario, we can use ML.NET Model Builder.
Developing chatbot or virtual assistant that can understand and respond to user queries. You can use Azure Open AI, and The Semantic kernel SDK to make it happen.
If you want to build one of the solution you can visit this Collections | Microsoft Learn to make you accelerate the development
When you want to create a solution that uses natural language processing (NLP), you can use a lot of open-source libraries. However, if you take a closer look at the Azure AI, they have NLP features through Azure AI language, and it starts from FREE.
Natural Language Processing (NLP) development with Azure AI involves utilizing Microsoft's suite of tools and services to build, deploy, and manage NLP models and applications. Azure offers a range of NLP-related services such as Azure Cognitive Services, Azure Machine Learning, and Azure Databricks, which provide capabilities for language understanding, sentiment analysis, named entity recognition, and more.
Using Azure AI for NLP development allows developers to harness the power of pre-built models and APIs for common NLP tasks, as well as the flexibility to build custom NLP models using machine learning frameworks like TensorFlow and PyTorch on Azure Machine Learning. Additionally, Azure provides infrastructure and tools for data processing, model training, and deployment, making it a comprehensive platform for NLP development.
By leveraging Azure AI for NLP development, businesses and developers can expedite the creation of language-aware applications, automate text analysis workflows, and gain insights from unstructured data sources. Azure's robust security and compliance features also ensure that NLP applications built on the platform adhere to industry standards and best practices. Overall, Azure AI empowers developers to create sophisticated NLP solutions while benefiting from the scalability, reliability, and performance of the Azure cloud platform.
To create an Azure AI Language project using Visual Studio, follow these steps:
Provision Azure Resources:
Create an Azure Subscription (you can create one for free).
Log into Language Studio.
If it’s your first time logging in, choose a language resource and select “Create a new language resource.” Provide details such as name, location, and resource group.
Use Language Studio with Your Own Text:
Once you’re ready to use Language Studio features on your text data, you’ll need an Azure AI-language resource for authentication and billing. I recommend you do not need to activate this because it needs to be paid, but of course, I recommend you to subscribe when the transaction goes up.
Follow the setup process to create your resource.
You can then call REST APIs and use client libraries programmatically. You can see a lot examples here Language Studio - Microsoft Azure
Remember to choose a location for your Azure AI language resources so the latency of the resources
Some NLP scenarios that you can expect:
Extract information that comes from the document/text. For example, you want to understand the main topic or contribution of an article
Classify text for sentiment analysis, language detection, and custom text classification. For example, you want to moderate content in the forum
Question and answer. For example, creating a Bot for simple question-and-answer.
Summarize information. For example, you want to create meeting notes based on the meeting documents / conversational text
Customize translation. For example, you want to create a translation of a natural language to cat language :D
In this article, we will discuss how to step through a multi-account Azure Environment for Cost Optimization. An organization might need to restructure its multi-accounts. Here is why.
Multi-Account means that the account belongs to each of the responsible roles. This is good when Azure's budget is separated between divisions.
Multi-Account means we can open as many subscriptions as we need. For example, one subscription is for Pay As You Go, one is for DevTest, and others are for Collaboration.
Multi-account means we can consolidate using manage organizations and have separate Microsoft Entra IDs. This is great for companies that build in Microsoft Azure for their customers. So any development won't mess up their Entra ID.
So here are the steps.
Create management groups. Manage your Azure subscriptions at scale with management groups - Azure Governance - Azure governance | Microsoft Learn
Structure your subscriptions, for example,
Research subscription - for research purposes.
Development Subscription - for development purposes
Your customer subscription—managed services for the production server. I recommend you create one subscription for customers with a huge workload.
Product subscription—This is an internal system in the organization, such as a website, internal information system, DevOps subscriptions, and many more.
Structure the organization by managing group roles Organize subscriptions into management groups and assign roles to users - Microsoft Defender for Cloud | Microsoft Learn
Create resource groups based on the Project. Manage resource groups - Azure portal - Azure Resource Manager | Microsoft Learn
Managing tags. The organization should have tags to consolidate tags for any activities such as "safe to delete" "production" "expired" "high transaction" etc. Tag resources, resource groups, and subscriptions with Azure portal - Azure Resource Manager | Microsoft Learn
This article is going with post for Azure Global Bootcamp 2023 that held in Cilacap Indonesia. On this session, I shared about how to use AI in Visual Studio 2022 and Visual Studio Codes, you can grab and see the decks on this post.
If you want to join my live session, you can join at https://bit.ly/globalazure2023 , see you there