9 min read
Content Understanding vs Document Intelligence: Choose by Shape
Document Intelligence wins on fixed layouts, Content Understanding on variable or multimodal input: split the pipeline and leave stable extractors alone.
Architecture
171 articles
8 min read
DiskANN in Azure SQL Is GA: Do You Still Need a Separate Vector Store?
DiskANN vector indexes are now GA in Azure SQL and Fabric SQL. When embeddings belong next to your rows, and when Azure AI Search still wins.
9 min read
Foundry Routines Are GA: Scheduled Agents Without Building a Scheduler
Foundry routines run an agent on a timer, a schedule or a GitHub or Teams event. When to use them, when to use a workflow, and guardrails for unattended runs.
9 min read
Egress Policy Belongs on the Agent, Not in Its Code
Foundry's preview egress controls attach ordered destination rules to a hosted agent. Why that limits prompt-injection damage, and how to roll it out safely.
9 min read
Foundry Hosted Agents Are GA: When to Stop Running Your Own Containers
Hosted agents in Foundry Agent Service are GA. How they compare with Agent Framework on Container Apps or AKS on identity, networking, cold starts and tracing.
9 min read
Event-Driven Refresh for Materialized Lake Views: Schedules Still Win
Event-triggered refresh for Fabric materialized lake views trades predictable capacity and clean ordering for freshness. Where events fit and where they don't.
8 min read
Harness Agent or Plain Agent? What Agent Framework Now Decides for You
The Agent Framework Harness bundles planning, memory, compaction, approvals and telemetry. When to accept its opinions and when a plain Agent is better.
8 min read
Findable by Design: Workspace Choices That Shape the OneLake Catalog
How workspace splits, item names, descriptions, endorsement and domains decide what people find in the OneLake catalog, plus a findability test you can script.
7 min read
Agent Fallbacks Are a Launch Requirement, Not an Ops Task
Decide what an agent does when a tool fails, a step runs out of budget or grounding is weak before launch, and make the release check fail without it.
8 min read
Where Agent State Breaks: Resume, Retries and Duplicate Runs
Typed state and checkpoints don't stop an agent paying twice: five places agent workflow state breaks at its boundaries, and patterns that hold.
8 min read
Ingestion vs Curation in Fabric: Where I Draw the Workspace Line
Why I split Fabric ingestion and curation at the landing table, when that means separate workspaces joined by shortcuts, and when one lakehouse is enough.
8 min read
Agent Tool Policy at the Gateway: MCP Through API Management
Tool rules inside agent code drift once several teams build agents. Put identity, rate limits and tracing for MCP tools in Azure API Management instead.
7 min read
Latency Budgets as a Cost Control: Pricing Each AI User Journey
Set a latency budget per user journey, then let it choose the Azure OpenAI deployment tier, model and reasoning effort you actually pay for.
8 min read
Fabric Pipeline Failure Paths: Catch the Error, Then Fail the Run
How dependency conditions decide a Fabric pipeline's final status, why try-catch hides failures, and how to log, alert and still fail the run honestly.
6 min read
Explicit State for Agent Workflows: Stop Making the Model Remember
Agents get confused when workflow state lives in the chat transcript. Four patterns for typed state, fresh context and code-owned transitions.
8 min read
Fabric Data Movement: Design for the Half-Finished Copy
How to design Fabric Copy jobs and copy activities so a failed, partial or repeated copy never leaves duplicates, gaps or a watermark that moved too far.
6 min read
Least-Privilege Tools for AI Agents: Four Layers of Control
Agent reliability depends on which tools a model can reach. Four layers of tool policy (surface, schema, approval, budget) with Agent Framework code.
8 min read
Foundry Project Boundaries: Draw Them Around Use Cases, Not Demos
How I split Microsoft Foundry resources and projects by environment and use case so RBAC, quota and agent data line up with ownership and delivery speeds up.
9 min read
Fabric Daily Loads: Gate on Data Readiness, Not the Clock
How to remove brittle dependencies from daily Fabric loads with one orchestrating pipeline, readiness checks, idempotent writes and deliberate retries.
9 min read
Transitions, Not Tasks: Designing AI for the Gap Between Meetings
Most AI assistants help inside a task. The expensive part of knowledge work is the switch between commitments, and that needs a different agent design.
9 min read
Azure AI Foundry Is Now Microsoft Foundry: Hubs, Projects, Choices
What the Microsoft Foundry rename means in early 2026: Foundry projects vs hubs, the preview portal, prompt flow's place, and how I'd decide.
8 min read
Fine-Tuning or RAG? Decide by the Failure You're Fixing
Fine-tuning and RAG fix different failures. How to tell which one you have on Microsoft Foundry in early 2026, what each costs, and when to combine them.
6 min read
Workflow First, Agent Inside: Putting the Model in One Step
Most agent designs only need autonomy in one step. How to build the workflow around it with Agent Framework executors, switch-case edges and loop caps.
8 min read
Azure OpenAI vs OpenAI in 2026: Same Code, Different Contract
With the Azure OpenAI v1 API one OpenAI client works on both, so the choice rests on data processing location, identity, networking, capacity and model timing.
8 min read
Agentic Retrieval or a Hand-Built RAG Pipeline? How I Decide
Azure AI Search knowledge bases now plan queries for you, in preview. When to hand over the RAG pipeline, when to keep building it, and how to test both.
9 min read
Databricks or Fabric in 2026: Choosing Your Primary Platform
A practical decision guide for picking Azure Databricks or Microsoft Fabric as your primary data platform in 2026, and when running both is worth the cost.
9 min read
Seven RAG Failure Modes and the Pattern That Fixes Each
Diagnose why a RAG system fails before adding patterns: hybrid search, semantic reranking, grounding gates and citations on Azure AI Search, mapped to failures.
9 min read
AI Agents in 2026: Where They Earn Their Keep and Where They Don't
A practitioner's reality check on AI agents going into 2026: the use cases that work, where they break, and how to pick a framework without the hype.
1 min read
API Design Patterns That Stood the Test of Time in 2025
Good API design is timeless. These patterns work because they prioritize developer experience and operational reliability.
1 min read
Boxing Day Deep Dive: Understanding Transformer Architecture
Transformers solve a key problem: how do you process sequences while understanding relationships between all elements, not just adjacent ones?
1 min read
Implementing Zero-Trust Security for AI Workloads on Azure
Zero-trust isn't optional for AI workloads handling sensitive data. Implement these patterns from day one to avoid costly retrofitting.
1 min read
Building Resilient Microservices with Azure Service Bus
Reliable messaging is the backbone of microservices architecture. Azure Service Bus provides enterprise-grade messaging, but using it effectively requires…
1 min read
Azure Container Apps vs Azure Kubernetes Service: When to Use Each
Choose Container Apps when: You want managed infrastructure Your team lacks Kubernetes expertise You have event driven or HTTP workloads You need rapid time…
1 min read
Building Event-Driven Architectures with Azure Event Grid and Functions
Event-driven architecture requires careful design but delivers superior scalability and resilience for distributed systems.
1 min read
September 2025 Recap: Key Takeaways for Enterprise AI
The most successful AI implementations share common architectural patterns. Hybrid search combining vector and keyword retrieval consistently outperforms…
1 min read
Vector Database Selection: Choosing the Right Solution for RAG
The best vector database depends on your specific requirements. Azure AI Search excels for hybrid search with integrated semantic ranking. Pinecone offers…
1 min read
Multi-Agent Systems: Orchestrating Specialized AI Agents
Multi-agent architectures shine for tasks requiring diverse expertise: research reports, code reviews, complex analysis, and creative projects. The key is…
1 min read
Building AI Agents with Tool Use: A Practical Architecture
At its core, an AI agent follows a simple loop: observe, think, act, repeat. Tools should be focused, well-documented, and handle errors gracefully.
1 min read
June Recap: Building Modern AI Systems
Understanding june recap is essential for production AI systems. Here's what you need to know.
1 min read
Data Lineage: Building Modern AI Systems
Understanding data lineage is essential for production AI systems. Here's what you need to know.
1 min read
Metadata Management: Building Modern AI Systems
Understanding metadata management is essential for production AI systems. Here's what you need to know.
1 min read
Data Governance: Building Modern AI Systems
Understanding data governance is essential for production AI systems. Here's what you need to know.
1 min read
Unity Catalog: Building Modern AI Systems
Understanding unity catalog is essential for production AI systems. Here's what you need to know.
1 min read
Iceberg Ai: Building Modern AI Systems
Understanding iceberg ai is essential for production AI systems. Here's what you need to know.
1 min read
Delta Lake Ai: Building Modern AI Systems
Understanding delta lake ai is essential for production AI systems. Here's what you need to know.
1 min read
Data Lakehouse Ai: Building Modern AI Systems
Understanding data lakehouse ai is essential for production AI systems. Here's what you need to know.
1 min read
Batch Processing: Building Modern AI Systems
Understanding batch processing is essential for production AI systems. Here's what you need to know.
1 min read
Caching Strategies: Building Modern AI Systems
Understanding caching strategies is essential for production AI systems. Here's what you need to know.
1 min read
Load Balancing Ai: Building Modern AI Systems
Understanding load balancing ai is essential for production AI systems. Here's what you need to know.
1 min read
Model Serving: Building Modern AI Systems
Understanding model serving is essential for production AI systems. Here's what you need to know.
1 min read
Gpu Optimization: Building Modern AI Systems
Understanding gpu optimization is essential for production AI systems. Here's what you need to know.
1 min read
Kubernetes Ai: Building Modern AI Systems
Understanding kubernetes ai is essential for production AI systems. Here's what you need to know.
1 min read
Container Apps Ai: Building Modern AI Systems
Understanding container apps ai is essential for production AI systems. Here's what you need to know.
1 min read
Durable Functions Ai: Building Modern AI Systems
Understanding durable functions ai is essential for production AI systems. Here's what you need to know.
1 min read
Azure Functions Ai: Building Modern AI Systems
Understanding azure functions ai is essential for production AI systems. Here's what you need to know.
1 min read
Serverless Ai: Building Modern AI Systems
Understanding serverless ai is essential for production AI systems. Here's what you need to know.
1 min read
Azure Event Grid Ai: Building Modern AI Systems
Understanding azure event grid ai is essential for production AI systems. Here's what you need to know.
1 min read
Kafka Ai: Building Modern AI Systems
Understanding kafka ai is essential for production AI systems. Here's what you need to know.
1 min read
Event Driven Ai: Building Modern AI Systems
Understanding event driven ai is essential for production AI systems. Here's what you need to know.
1 min read
Websocket Ai: Building Modern AI Systems
Understanding websocket ai is essential for production AI systems. Here's what you need to know.
1 min read
Streaming Inference: Building Modern AI Systems
Understanding streaming inference is essential for production AI systems. Here's what you need to know.
1 min read
Real Time Ai: Building Modern AI Systems
Understanding real time ai is essential for production AI systems. Here's what you need to know.
1 min read
Agent Memory Systems: Building Persistent AI Memory
Sophisticated memory systems enable agents to learn and improve over time.
1 min read
Advanced Agent Patterns: Complex Multi-Agent Systems
Choose the right execution pattern based on task complexity and agent capabilities.
1 min read
Edge AI Architecture: Designing for On-Device Intelligence
Edge AI architecture balances latency, privacy, and capability across processing tiers.
1 min read
Model Routing Patterns: Smart Query Distribution
Smart routing can reduce AI costs by 50-70% while maintaining quality.
1 min read
SLM vs LLM Strategies: Choosing the Right Model Size
The right model size depends on your specific requirements, not just capabilities.
1 min read
Semantic Layer for AI: Building Business-Ready Data Interfaces
A semantic layer enables AI to understand business terminology and generate accurate queries.
1 min read
Data Mesh and AI: Decentralized Intelligence at Scale
Data mesh with AI enables domain teams to own both data and intelligence.
1 min read
Knowledge Graphs and AI: Building Intelligent Data Structures
Knowledge graphs add structured reasoning capabilities to AI applications.
1 min read
RAG Advanced Patterns: Beyond Basic Retrieval
Advanced RAG combines query expansion, hybrid retrieval, reranking, and context compression for better results.
1 min read
AI Orchestration Frameworks: Building Complex AI Workflows
Choose orchestration frameworks based on workflow complexity and team expertise.
1 min read
Event-Driven AI Integration: Connecting AI to Business Events
Event-driven AI enables intelligent, automated responses to business events. Start with high-value events where AI can add immediate value.
1 min read
Real-Time AI Patterns: Building Low-Latency Intelligent Systems
Real-time AI requires discipline around latency. Design for the worst case and optimize for the common case.
1 min read
SLM vs LLM: Choosing the Right Model Size Strategy
The right model isn't always the biggest one. Match model capability to task requirements for optimal cost, latency, and quality.
1 min read
Event-Driven AI: Building Reactive Intelligent Systems
Event-driven AI enables responsive, intelligent systems. Start with simple event handlers and evolve to complex orchestration as needed.
8 min read
Where Should Your Vectors Live on Azure? Search Index vs Database
Azure AI Search, Cosmos DB for NoSQL and PostgreSQL with pgvector can all hold embeddings now. Here is how I decide between a search index and your database.
7 min read
GraphRAG 1.x: When Global Search Justifies the Indexing Bill
How Microsoft's GraphRAG 1.x builds a knowledge graph, what local, global and DRIFT search do, and when its indexing cost is worth paying over vector RAG.
8 min read
Five LLM Application Patterns, From Least to Most Autonomy
A practical ladder of LLM patterns on Azure in January 2025: structured outputs, grounded RAG, tool workflows, self-review and agents.
7 min read
Where Streaming Logic Belongs in Fabric: Eventstream, KQL or Spark
A decision guide for placing filters, enrichment, aggregation and alerts across Eventstream, Eventhouse and Spark in Fabric Real-Time Intelligence.
7 min read
Multi-Agent Orchestration: Pick the Pattern Before the Framework
Four multi-agent orchestration patterns, what Semantic Kernel, AutoGen 0.4 and Azure AI Agent Service offered for each in January 2025, and when to skip them.
1 min read
The Future of Data Platforms: Where We're Headed
The data platform of the future will feel like working with a knowledgeable colleague who understands your data and your business. Start building toward…
1 min read
Data Platform Trends: What Shaped 2024 and What's Coming
The data platform landscape is consolidating around unified, AI-native platforms with strong real-time capabilities. Organizations should align their…
1 min read
Scaling AI Systems: From Prototype to Enterprise
Scaling AI Systems: From Prototype to Enterprise
1 min read
Building AI Applications: Choosing the Right Platform and Architecture
The key to successful AI applications is choosing the right architecture for your requirements and building with production considerations from the start.
1 min read
Microsoft Fabric Architecture Evolution: Past, Present, and Future
Microsoft Fabric represents the natural evolution toward unified, AI-powered data platforms. Understanding this evolution helps you make informed decisions…
1 min read
Building a Unified Data Platform with Microsoft Fabric
A unified data platform on Fabric eliminates the complexity of managing multiple systems while providing the flexibility to handle any data workload.
1 min read
HTAP Patterns in Microsoft Fabric: Unifying Transactions and Analytics
HTAP in Microsoft Fabric enables unified transactional and analytical workloads without complex ETL pipelines. Design your schemas with both workloads in mind.
1 min read
Agentic AI Capabilities: From Chatbots to Autonomous Agents
Agentic capabilities transform AI from a question-answering system into an autonomous problem-solver. The key is combining planning, tools, memory, and…
1 min read
Circuit Breakers for AI Systems: Preventing Cascade Failures
Circuit breakers are essential for AI systems that depend on external APIs. They prevent resource exhaustion, protect downstream services, and enable…
1 min read
Graceful Degradation in AI Systems: Maintaining Service Quality
Graceful degradation is about providing the best possible experience given current constraints. Plan for every level of degradation and communicate clearly…
1 min read
Fallback Patterns for AI Applications: Ensuring Continuity
Fallback patterns ensure your AI application remains useful even when primary services fail. Design your fallbacks to maintain the best possible user…
1 min read
Agent Reliability Patterns: Building Robust AI Systems
Building reliable agents requires investment in error handling, recovery mechanisms, and observability. These patterns form the foundation for production AI…
1 min read
Real-Time Sync Patterns in Microsoft Fabric
For sub second latency: For minute level latency with relational sources: Combine streaming and batch for different data types: Distribute data to multiple…
1 min read
Memory Consolidation for AI Agents: From Short-Term to Long-Term
1. Set appropriate thresholds : Not everything needs to be remembered 2. Consolidate regularly : Don't let short term memory overflow 3. Abstract…
1 min read
Agent Memory Patterns: Beyond Simple Context
Periodically consolidate memories for efficiency: 1. Layer your memory : Different types for different purposes 2. Manage capacity : Always have eviction…
1 min read
Graph-Based AI Agents: Design Patterns and Architecture
Think of your agent as a state machine: Nodes : Processing steps (functions that transform state) Edges : Transitions between steps State : Accumulated…
5 min read
LLM Routing Strategies for Production Systems
The simplest approach: fixed rules based on task type. Pros: Simple, predictable, easy to debug Cons: Doesn't adapt, requires manual tuning
1 min read
Multi-Model Orchestration: Using Multiple LLMs Effectively
Different tasks need different capability levels. Using GPT-4o for simple classifications wastes money.
1 min read
Data Mesh with Microsoft Fabric
1. Clear domain boundaries Define ownership clearly 2. Product thinking Treat data as a product 3. Self serve enablement Empower domain teams 4.…
1 min read
AI Agent Best Practices: Lessons from Production
AI Agent Best Practices: Lessons from Production
1 min read
Multi-Agent Architectures: Collaboration at Scale
Single agents have limits. Multi-agent systems multiply capabilities. Today I'm exploring architectures for agent collaboration.
1 min read
Agent Orchestration Patterns for Production
Building single agents is one thing. Orchestrating multiple agents for complex workflows is another. Today I'm exploring production-ready orchestration…
1 min read
Building AI Agents: From Simple to Sophisticated
AI agents are systems that can take actions autonomously to achieve goals. Today I'm exploring how to build agents from simple tool-using assistants to…
1 min read
Open Source vs Proprietary LLMs: Making the Right Choice
The choice between open-source and proprietary LLMs is one of the most important architectural decisions for AI projects. Let's analyze the tradeoffs…
1 min read
Provisioned Throughput Deep Dive: Architecture and Implementation
1. Multi region deployment Distribute for resilience 2. Monitor continuously Track utilization and latency 3. Plan scaling windows PTU changes aren't…
1 min read
LLM Model Selection Criteria: A Decision Framework
Model selection should be systematic, not arbitrary. Define your requirements, score candidates objectively, and document decisions for future review.
1 min read
Multi-LLM Strategies: Building Resilient AI Applications
Resilience : Failover when one provider has outages Cost optimization : Route to cheaper models when possible Capability matching : Use best model for each…
8 min read
Fabric Eventstream Patterns: Deciding Where Stream Logic Lives
Practical Fabric Eventstream patterns: what belongs in the no-code event processor, what belongs in KQL, and what to fix before events arrive.
7 min read
When to Split One AI Agent into Several: A Supervisor Pattern
When a single GPT-4 agent stops being reliable, split it into specialists. A framework-free supervisor pattern on Azure OpenAI, with trade-offs and limits.
7 min read
A RAG Maturity Model: Diagnosing Where Your System Falls Short
A five-level RAG maturity model for Azure AI Search and Azure OpenAI, covering what each level fixes, what it costs, and when to stop climbing.
7 min read
Owning the Loop: AI Assistant Architecture on Chat Completions
How to structure a production AI assistant on Azure OpenAI Chat Completions: conversation state, retrieval, tool calling and the orchestration loop you own.
1 min read
Data Platform Evolution: Where We're Heading
Having lived through the shift from monolithic warehouses to lakehouses, the pattern I'm seeing now is convergence: open formats, governance, and…
1 min read
Fabric Components: When to Use What
Fabric's component set is broad; the hard part is picking the minimal surface that solves your business need. This guide distils when to use Lakehouse…
1 min read
Lakehouse vs Warehouse in Fabric: Making the Right Choice
Choosing Lakehouse vs Warehouse is an architectural trade-off: for open-format, large-scale analytics and ML, Lakehouse is the better fit; for classic T-SQL…
1 min read
Fabric Architecture Patterns: Designing for Scale
Designing for scale in Fabric is about decomposition and clear contracts between layers. The medallion architecture works well: small, composable…
1 min read
Microsoft Fabric Best Practices for Production
Shipping Fabric into production exposes common anti-patterns — unbounded capacity use, duplicated data copies, and insufficient observability. The best…
1 min read
Azure OpenAI Enterprise Patterns: Lessons from Production Deployments
After months of working with Azure OpenAI in enterprise environments, the patterns that separate solid deployments from problematic ones have become clear …
2 min read
Content Moderation Patterns for AI Applications
Design patterns and best practices for implementing content moderation in AI-powered applications.
1 min read
Understanding Runnable Interfaces in LangChain
Deep dive into LangChain's Runnable interface and its implementations for building flexible LLM pipelines.
1 min read
Memory Management for LLM Applications
Tomorrow we'll explore LangChain updates and new features. LangChain Memory Vector Stores Conversational Memory Patterns
1 min read
Rate Limit Management for AI APIs
Tomorrow we'll explore token estimation techniques. Rate Limiting Patterns Circuit Breaker Pattern Azure OpenAI Best Practices
1 min read
Circuit Breakers for AI Applications: Preventing Cascade Failures
This concludes our June 2023 series on Azure, Data, and AI topics. We covered: Microsoft Fabric deep dives (Lakehouse, Delta Lake, OneLake) Power BI Direct…
1 min read
Fallback Patterns for AI Applications
Fallback patterns ensure continuous service availability. Tomorrow, I will cover circuit breakers for AI applications.
1 min read
Conversation Management: Building Production Chat Systems
Robust conversation management enables reliable production chat systems. Tomorrow, I will cover context pruning strategies.
1 min read
Building AI Agents: From Chatbots to Autonomous Assistants
Function calling, released last week with the 0613 model versions, gives AI agents a proper foundation — and I've been rebuilding some agent prototypes to…
1 min read
AI Orchestration Patterns: Coordinating Multi-Model Systems
AI orchestration patterns enable building sophisticated AI systems from modular components. The key is designing for reliability, observability, and…
1 min read
Model Deployment Patterns for Azure OpenAI
These deployment patterns enable safe, controlled rollouts of LLM application changes. Start with gateway and blue-green patterns, then add canary and A/B…
1 min read
Map-Reduce Patterns for LLM Applications
Map-reduce transforms complex LLM tasks into manageable, parallelizable operations. Master these patterns for processing data at any scale.
1 min read
Context Window Management: 8K vs 32K Strategies
The 32K model costs 2x more per token. Use it strategically. Effective context management balances quality, cost, and capability. Master these patterns to…
1 min read
Enterprise Patterns for Azure OpenAI Applications
Deploy across regions for resilience: Configure Azure API Management for governance: Enterprise Azure OpenAI deployments need: 1. Multi region failover for…
1 min read
Vector Databases for AI Applications
Vectors (embeddings) represent meaning in high-dimensional space. Similar items have similar vectors.
1 min read
Understanding ChatGPT Plugins Architecture
The plugin ecosystem is just beginning. Now is the time to experiment and understand the patterns.
1 min read
RAG Architecture Patterns: Building Production-Ready Systems
The basic pattern for straightforward use cases: Transform queries for better retrieval: Generate a hypothetical answer first, then use it for retrieval:…
7 min read
RAG on Azure OpenAI: Grounding GPT-3 Answers in Your Own Documents
What retrieval augmented generation is, why it beats fine-tuning for company knowledge, and a minimal loop on Azure OpenAI completions in January 2023.
8 min read
Do You Need a Vector Database Yet? ANN Indexes and Trade-offs
What a vector database adds over brute force: ANN indexes, recall, filtering and updates, and how Pinecone, Weaviate, Milvus, Qdrant and pgvector compare.
7 min read
Azure OpenAI Is GA: What Changed on 16 January and What Didn't
Azure OpenAI Service reached GA on 16 January 2023: what that changes for production plans, what still needs an approved application, and how to proceed.
6 min read
Completions Now, Chat Later: Keeping Azure OpenAI Code Portable
Azure OpenAI is GA with a Completions API only, and ChatGPT is coming soon. How to decide what needs chat and keep your prompt code ready for the change.
7 min read
Throttling in the Azure OpenAI Preview: Pacing Per-Deployment Requests
Azure OpenAI preview limits are requests per second per deployment. How to pace calls, retry 429s properly and scale out with the openai 0.26 Python library.
7 min read
Azure OpenAI vs OpenAI API: What Differs in January 2023
Azure OpenAI or OpenAI's own API in January 2023? Access, models, data use, cost and code compared, plus one Python client that can target either.
7 min read
ChatGPT-Style Chat on Azure OpenAI Without a Chat API
There is no ChatGPT API yet. Here is how to build multi-turn chat on Azure OpenAI's Completions API: transcripts, state, token budgets and streaming.
1 min read
Data Engineering Patterns That Defined 2022
2022 established these patterns as the foundation of modern data engineering. The lakehouse became the default architecture for new data platforms. Data…
1 min read
The Serverless Benefits Realized in 2022
The promise of serverless has largely been fulfilled: Pay only for what you use No server management Automatic scaling Faster time to market Most…
1 min read
Cloud Native Maturity: Where Organizations Stand in 2022
Lift and shift migrations VMs in the cloud Traditional architectures Using managed services Basic automation Some containerization Microservices…
1 min read
Hybrid Cloud Strategies with Azure
Hybrid cloud strategies provide flexibility while meeting compliance and performance requirements.
1 min read
Graph Database Fundamentals for Azure Developers
Graph databases unlock powerful relationship-based queries that would be complex or impossible with traditional relational approaches.
1 min read
Designing End-to-End IoT Solution Architecture on Azure
This architecture provides a scalable, secure foundation for enterprise IoT solutions on Azure.
1 min read
Edge Computing Database Patterns and Best Practices
Handle intermittent connectivity by storing data locally and forwarding when connected. Reduce data volume by aggregating at the edge before cloud sync.
1 min read
Database Patterns for IoT Applications
These patterns form the foundation for building scalable, performant IoT database solutions.
1 min read
High Availability Architecture for MySQL on Azure
Implementing these HA patterns ensures your MySQL applications remain resilient during infrastructure failures.
1 min read
Understanding Hyperscale Citus Architecture
Understanding this architecture helps you make informed decisions about data modeling and query optimization in your distributed PostgreSQL deployments.
1 min read
DNS Forwarding Strategies for Azure Hybrid Architectures
1. Document all forwarding rules : Maintain a central registry 2. Use redundant DNS servers : Always specify multiple targets 3. Monitor DNS query latency :…
1 min read
Azure Private Link Service: Expose Your Services Privately
Private Endpoint : Consume services privately (you're the client) Private Link Service : Expose services privately (you're the provider) 1. SaaS providers :…
1 min read
Power Platform Environment Strategy: Planning for Scale
A well-planned environment strategy enables teams to move fast while maintaining governance and security.
1 min read
Power Platform Solution Layering: Managing Dependencies
Understanding solution layering prevents deployment issues and ensures predictable behavior across environments.
7 min read
Well-Architected Reviews as a Habit, Not a Kickoff Ritual
How to run Azure Well-Architected Reviews on a cadence: what the assessment covers, what Azure Advisor adds between reviews, and what neither can tell you.
7 min read
Azure Landing Zones: Accelerator, Terraform Module or Your Own Bicep
What an Azure landing zone is in early 2022, and how to choose between the portal accelerator, the Terraform module and your own Bicep.
8 min read
Designing for Eviction: Architecture Patterns for Azure Spot VMs
Spot VMs only save money if the design expects eviction: a regular base tier, diversified Spot pools, queue-driven workers and a Scheduled Events handler.
1 min read
Event-Driven Architecture Patterns for Modern Applications
Event-driven architecture in 2021 moved from architectural pattern to practical implementation. The tools support it, the patterns are proven, and the…
2 min read
Log Analytics Workspace Design for Enterprise Kubernetes
Log Analytics workspace design is one of those infrastructure decisions that feels low-stakes until your bill arrives or you hit a query that crosses a…
2 min read
Azure Functions Premium Plan - When and Why
The Azure Functions Premium plan is the hosting option I reach for as soon as two of these three conditions are true: the function needs to connect to a…
1 min read
Consumption vs Premium Azure Functions - Making the Right Choice
Feature Consumption Premium Scaling 0 to 200 instances 1 to 100 instances Cold starts Yes No (pre warmed) Max timeout 10 min (default 5) 60 min (default 30)…
1 min read
Background Processing Patterns in Azure
Use queues to handle variable load: Scale processing with multiple consumers: Process high priority items first: Coordinate long running business processes:…
1 min read
Event Sourcing Patterns with Azure Cosmos DB
Event Sourcing with Azure Cosmos DB provides a robust foundation for building systems that require complete audit trails, temporal queries, and event driven…
1 min read
CQRS Pattern Implementation with Azure Services
CQRS with Azure services enables building highly scalable applications with optimized read and write paths. Azure SQL provides transactional guarantees for…
1 min read
Leveraging Azure Architecture Center Reference Architectures
The Azure Architecture Center is the reference resource I bookmark more than any other when I need to move from "I know what I want to build" to "I know the…
1 min read
Implementing the Azure Well-Architected Framework
The Azure Well-Architected Framework is the document I send to clients at the start of every architecture engagement and the checklist I review before every…
1 min read
Multi-Tenant Management Patterns in Azure
Multi-tenant Azure management is the architecture problem I've spent more time on this year than any other. The patterns matter because the wrong choice…
8 min read
Designing Domain Events for Azure Event Grid Custom Topics
How to design the event contract, topic boundaries, filters and consumers for Azure Event Grid custom topics, and which .NET SDK to use in January 2021.
1 min read
Azure Functions Dependency Injection: Clean Architecture
The first Azure Function project I shipped used static everything. Static config, static SQL helpers, static logger. It worked—until I tried to write a unit…