Scaling Smart with Azure: Architecture That Works
Hi Tech Community! I’m Zainab, currently based in Abu Dhabi and serving as Vice President of Finance & HR at Hoddz Trends LLC a global tech solutions company headquartered in Arkansas, USA. While I lead on strategy, people, and financials, I also roll up my sleeves when it comes to tech innovation. In this discussion, I want to explore the real-world challenges of scaling systems with Microsoft Azure. From choosing the right architecture to optimizing performance and cost, I’ll be sharing insights drawn from experience and I’d love to hear yours too. Whether you're building from scratch, migrating legacy systems, or refining deployments, let’s talk about what actually works.
Error Running Script in Runbook with System Assigned Managed Identity
Hello everyone, I could use some assistance, please. I'm encountering an error when trying to run a script within a runbook. I'm using PowerShell 5.1 with a system-assigned managed identity. The script works find without using the managed identiy via powershell outside of azure. Error: System.Management.Automation.ParameterBindingException: Cannot process command because of one or more missing mandatory parameters: Credential. at System.Management.Automation.CmdletParameterBinderController.PromptForMissingMandatoryParameters(Collection1 fieldDescriptionList, Collection1 missingMandatoryParameters) at System.Management.Automation.CmdletParameterBinderController.HandleUnboundMandatoryParameters I am using this script Connect-ExchangeOnline -ManagedIdentity -Organization domain removed for privacy reasons # Specify the user's mailbox identity $mailboxIdentity = "email address removed for privacy reasons" # Get mailbox configuration and statistics for the specified mailbox $mailboxConfig = Get-Mailbox -Identity $mailboxIdentity $mailboxStats = Get-MailboxStatistics -Identity $mailboxIdentity # Check if TotalItemSize and ProhibitSendQuota are not null and extract the sizes if ($mailboxStats.TotalItemSize -and $mailboxConfig.ProhibitSendQuota) { $totalSizeBytes = $mailboxStats.TotalItemSize.Value.ToString().Split("(")[1].Split(" ")[0].Replace(",", "") -as [double] $prohibitQuotaBytes = $mailboxConfig.ProhibitSendQuota.ToString().Split("(")[1].Split(" ")[0].Replace(",", "") -as [double] # Convert sizes from bytes to gigabytes $totalMailboxSize = $totalSizeBytes / 1GB $mailboxWarningQuota = $prohibitQuotaBytes / 1GB # Check if the mailbox size exceeds 90% of the warning quota if ($totalMailboxSize -ge ($mailboxWarningQuota * 0.0)) { # Send an email notification $emailBody = "The mailbox $($mailboxIdentity) has reached $($totalMailboxSize) GB, which exceeds 90% of the warning quota." Send-MailMessage -To "email address removed for privacy reasons" -From "email address removed for privacy reasons" -Subject "Mailbox Size Warning" -Body $emailBody -SmtpServer "smtp.office365.com" -Port 587 -UseSsl -Credential (Get-Credential) } } else { Write-Host "The required values(TotalItemSize or ProhibitSendQuota) are not available." }Comparision on Azure Cloud Sync and Traditional Entra connect Sync.
Introduction In the evolving landscape of identity management, organizations face a critical decision when integrating their on-premises Active Directory (AD) with Microsoft Entra ID (formerly Azure AD). Two primary tools are available for this synchronization: Traditional Entra Connect Sync (formerly Azure AD Connect) Azure Cloud Sync While both serve the same fundamental purpose, bridging on-prem AD with cloud identity, they differ significantly in architecture, capabilities, and ideal use cases. Architecture & Setup Entra Connect Sync is a heavyweight solution. It installs a full synchronization engine on a Windows Server, often backed by SQL Server. This setup gives administrators deep control over sync rules, attribute flows, and filtering. Azure Cloud Sync, on the other hand, is lightweight. It uses a cloud-managed agent installed on-premises, removing the need for SQL Server or complex infrastructure. The agent communicates with Microsoft Entra ID, and most configurations are handled in the cloud portal. For organizations with complex hybrid setups (e.g., Exchange hybrid, device management), is Cloud Sync too limited?Building an AI-Powered ESG Consultant Using Azure AI Services: A Case Study
In today's corporate landscape, Environmental, Social, and Governance (ESG) compliance has become increasingly important for stakeholders. To address the challenges of analyzing vast amounts of ESG data efficiently, a comprehensive AI-powered solution called ESGai has been developed. This blog explores how Azure AI services were leveraged to create a sophisticated ESG consultant for publicly listed companies. https://youtu.be/5-oBdge6Q78?si=Vb9aHx79xk3VGYAh The Challenge: Making Sense of Complex ESG Data Organizations face significant challenges when analyzing ESG compliance data. Manual analysis is time-consuming, prone to errors, and difficult to scale. ESGai was designed to address these pain points by creating an AI-powered virtual consultant that provides detailed insights based on publicly available ESG data. Solution Architecture: The Three-Agent System ESGai implements a sophisticated three-agent architecture, all powered by Azure's AI capabilities: Manager Agent: Breaks down complex user queries into manageable sub-questions containing specific keywords that facilitate vector search retrieval. The system prompt includes generalized document headers from the vector database for context. Worker Agent: Processes the sub-questions generated by the Manager, connects to the vector database to retrieve relevant text chunks, and provides answers to the sub-questions. Results are stored in Cosmos DB for later use. Director Agent: Consolidates the answers from the Worker agent into a comprehensive final response tailored specifically to the user's original query. It's important to note that while conceptually there are three agents, the Worker is actually a single agent that gets called multiple times - once for each sub-question generated by the Manager. Current Implementation State The current MVP implementation has several limitations that are planned for expansion: Limited Company Coverage: The vector database currently stores data for only 2 companies, with 3 documents per company (Sustainability Report, XBRL, and BRSR). Single Model Deployment: Only one GPT-4o model is currently deployed to handle all agent functions. Basic Storage Structure: The Blob container has a simple structure with a single directory. While Azure Blob storage doesn't natively support hierarchical folders, the team plans to implement virtual folders in the future. Free Tier Limitations: Due to funding constraints, the AI Search service is using the free tier, which limits vector data storage to 50MB. Simplified Vector Database: The current index stores all 6 files (3 documents × 2 companies) in a single vector database without filtering capabilities or schema definition. Azure Services Powering ESGai The implementation of ESGai leverages multiple Azure services for a robust and scalable architecture: Azure AI Services: Provides pre-built APIs, SDKs, and services that incorporate AI capabilities without requiring extensive machine learning expertise. This includes access to 62 pre-trained models for chat completions through the AI Foundry portal. Azure OpenAI: Hosts the GPT-4o model for generating responses and the Ada embedding model for vectorization. The service combines OpenAI's advanced language models with Azure's security and enterprise features. Azure AI Foundry: Serves as an integrated platform for developing, deploying, and governing generative AI applications. It offers a centralized management centre that consolidates subscription information, connected resources, access privileges, and usage quotas. Azure AI Search (formerly Cognitive Search): Provides both full-text and vector search capabilities using the OpenAI ada-002 embedding model for vectorization. It's configured with hybrid search algorithms (BM25 RRF) for optimal chunk ranking. Azure Storage Services: Utilizes Blob Storage for storing PDFs, Business Responsibility Sustainability Reports (BRSRs), and other essential documents. It integrates seamlessly with AI Search using indexers to track database changes. Cosmos DB: Employs MongoDB APIs within Cosmos DB as a NoSQL database for storing chat history between agents and users. Azure App Services: Hosts the web application using a B3-tier plan optimized for cost efficiency, with GitHub Actions integrated for continuous deployment. Project Evolution: From Concept to Deployment The development of ESGai followed a structured approach through several phases: Phase 1: Data Cleaning Extracted specific KPIs from XML/XBRL datasets and BRSR reports containing ESG data for 1,000 listed companies Cleaned and standardized data to ensure consistency and accuracy Phase 2: RAG Framework Development Implemented Retrieval-Augmented Generation (RAG) to enhance responses by dynamically fetching relevant information Created a workflow that includes query processing, data retrieval, and response generation Phase 3: Initial Deployment Deployed models locally using Docker and n8n automation tools for testing Identified the need for more scalable web services Phase 4: Transition to Azure Services Migrated automation workflows from n8n to Azure AI Foundry services Leveraged Azure's comprehensive suite of AI services, storage solutions, and app hosting capabilities Technical Implementation Details Model Configurations: The GPT model is configured with: Model version: 2024-11-20 Temperature: 0.7 Max Response Token: 800 Past Messages: 10 Top-p: 0.95 Frequency/Presence Penalties: 0 The embedding model uses OpenAI-text-embedding-Ada-002 with 1536 dimensions and hybrid semantic search (BM25 RRF) algorithms. Cost Analysis and Efficiency A detailed cost breakdown per user query reveals: App Server: $390-400 AI Search: $5 per query RAG Query Processing: $4.76 per query Agent-specific costs: Manager: $0.05 (30 input tokens, 210 output tokens) Worker: $3.71 (1500 input tokens, 1500 output tokens) Director: $1.00 (600 input tokens, 600 output tokens) Challenges and Solutions The team faced several challenges during implementation: Quota Limitations: Initial deployments encountered token quota restrictions, which were resolved through Azure support requests (typically granted within 24 hours). Cost Optimization: High costs associated with vectorization required careful monitoring. The team addressed this by shutting down unused services and deploying on services with free tiers. Integration Issues: GitHub Actions raised errors during deployment, which were resolved using GitHub's App Service Build Service. Azure UI Complexity: The team noted that Azure AI service naming conventions were sometimes confusing, as the same name is used for both parent and child resources. Free Tier Constraints: The AI Search service's free tier limitation of 50MB for vector data storage restricts the amount of company information that can be included in the current implementation. Future Roadmap The current implementation is an MVP with several areas for expansion: Expand the database to include more publicly available sustainability reports beyond the current two companies Optimize token usage by refining query handling processes Research alternative embedding models to reduce costs while maintaining accuracy Implement a more structured storage system with virtual folders in Blob storage Upgrade from the free tier of AI Search to support larger data volumes Develop a proper schema for the vector database to enable filtering and more targeted searches Scale to multiple GPT model deployments for improved performance and redundancy Conclusion ESGai demonstrates how advanced AI techniques like Retrieval-Augmented Generation can transform data-intensive domains such as ESG consulting. By leveraging Azure's comprehensive suite of AI services alongside a robust agent-based architecture, this solution provides users with actionable insights while maintaining scalability and cost efficiency. https://youtu.be/5-oBdge6Q78?si=Vb9aHx79xk3VGYAh
Entra: Lock screen help.
Hi guys, I need some assistance with entra regarding the lockscreen images. We had a previous lock screen which displayed the company logo and users were not allowed to change the lock screen, we needed it to be disabled and I deleted the script as well as the policy for the lock screen to try and remove it. However this hasn't worked, the lock screen is still displaying on all devices, and users cannot change the lockscreen. I do not want to perform a reset, because we have so many machines. Any advice on how to enable the users to edit the lock screen again or load a new policy, will be highly appreciated. What I have tried: Removing registry key for lock screen. (Key just pops up after restart) Loading a new script (Fails to load, no reason given, I suspect because it conflicts with old one) Disconnecting from entra and trying to edit the lock screen. Thanks.
Troubleshooting Azure Function App Proxy with Private Blob Container Access for Static Web App
Recently, I shared a problem I’m facing in my testing environment with a friend. I’ve decided to bring this issue to an open forum discussion to gather additional insights. I hope you can help me figure out what might be missing in my configuration. **Context:** I’m trying to replicate a solution in my test environment but encountering difficulties in a specific scenario. **Scenario:** I have a Function App acting as a proxy for a Static Web App hosted in a Blob Container. This Blob Container is set to private access, meaning public access is disabled. **The Problem:** The goal is for my Function App to authorize users and direct them correctly to the Static Web App. However, it’s not working as expected. **What I’ve tried so far:** 1. Configured Managed Identity for the Function App and granted the necessary permissions to the Blob Container. 2. Properly set up authentication and created the App Registration, which works flawlessly. 3. Verified that the proxy functions correctly when the Blob Container’s public access is enabled. **Current behavior:** - When public access to the Blob Container is enabled, everything works fine. - When public access is disabled, even with the proxy configured, access fails, and an error message "resource not found" is returned. **My questions are:** 1. Do I need to configure something additional in the proxy definition file? 2. Is there a specific setting, like a private endpoint or something similar, that I should implement to resolve this issue? **Additional considerations:** I haven’t configured a private endpoint yet, but I’m considering whether this would be the most appropriate solution for my case. My initial expectation was that granting the necessary permissions to the Function App via Managed Identity would solve the issue, but it hasn’t. I appreciate any guidance or suggestions you can provide!
Former Employer Abuse
My former employer, Albert Williams, president of American Security Force Inc., keeps adding my outlook accounts, computers and mobile devices to the company's azure cloud even though I left the company more than a year ago. What can I do to remove myself from his grip? Does Microsoft have a solution against abusive employers?Creating Logic App to Identify Low Storage Devices from Intune
Hello everyone, I’m seeking some assistance with creating a Logic App. I need to identify devices in Intune that have 5GB or less of available space and receive an email with the details of these devices, including their names. Is this achievable?Today on Azure Friday: Azure SignalR Service
Xiaokai He joins Scott Hanselman to discuss Azure SignalR Service. See how easy it is to start building your real-time web application without setting up your own SignalR server. For more information, see: Azure SignalR Service product page Azure SignalR samples (GitHub) ASP.NET SignalR What is Azure SignalR Service (docs) Azure SignalR Service pricing Create a free account (Azure)
