Azure Counsel Podcast

Bhanu Prakash - Azure Counsel
  • 0.0 (0)
  • How To
  • Bimonthly

Azure Counsel Podcast brings you practical tutorials and deep dives into Microsoft Azure, serverless computing, Event Hubs, IoT, and cloud architecture. Learn how to build scalable, real-world applications using Azure Functions, Service Bus, Cosmos DB, and more. Perfect for developers, architects, and cloud professionals seeking hands-on guidance, automation tips, and workflow optimizations. Watch the full video of each episode on my Youtube channel: https://www.youtube.com/@azurecounsel

  1. Oct 29

    Terraform vs Bicep for Azure Landing Zones: Which IaC Tool Should Cloud Architects Choose? Drift, State, Governance & Enterprise Scale

    Terraform vs Bicep for Azure Landing Zones: Which Infrastructure as Code (IaC) tool should cloud architects choose? Choosing between Terraform and Bicep for Azure Landing Zones is not as simple as saying “Azure means Bicep” or “multi-cloud means Terraform.” Both tools can provision enterprise Azure environments. The real architectural challenge is determining which approach your organisation can operate securely, recover reliably, govern consistently, and maintain over the next five years. In this Azure Counsel architecture deep-dive, we move beyond syntax comparisons and examine the real-world engineering decisions that shape an enterprise Infrastructure as Code strategy. We explore configuration drift, resource ownership, deployment failures, CI/CD security, team boundaries, brownfield adoption, governance blast radius, module evolution, and subscription vending at scale. If you are designing Azure Landing Zones or standardising how multiple teams deploy and manage Azure infrastructure, this episode gives you a practical framework for evaluating Terraform vs Bicep using evidence from your own environment. 🏗️ WHAT YOU'LL LEARN: TERRAFORM VS BICEP • Azure Landing Zones: How Terraform and Bicep fit into enterprise Azure platform architecture, governance, management groups, subscriptions, and shared platform services. • Infrastructure as Code (IaC): Why choosing an IaC tool is an operating-model decision, not just a question of language syntax. • Configuration Drift: What happens when engineers modify Azure resources outside the deployment pipeline, and how each approach handles reconciliation and resource ownership. 👨‍💻 WHO SHOULD LISTEN? • Azure Architects designing enterprise Azure Landing Zones• Cloud Architects evaluating Infrastructure as Code strategies• Platform Engineers building governed, reusable Azure platforms• DevOps Engineers designing secure infrastructure deployment pipelines• Azure Infrastructure Engineers managing large-scale environments• Technical Leads deciding between Terraform and Bicep• Engineers preparing for enterprise Azure architecture responsibilities and AZ-305-level design discussions 🎓 ABOUT AZURE COUNSEL Azure Counsel delivers practical, production-focused insights into Microsoft Azure architecture, cloud engineering, serverless computing, platform engineering, and event-driven systems. We go beyond introductory tutorials to explore the architectural trade-offs, failure modes, governance challenges, and engineering decisions that matter in real enterprise environments. Follow Azure Counsel for more deep-dives into: ✅ Azure Landing Zones and cloud governance✅ Terraform vs Bicep and Infrastructure as Code✅ Azure API Management and enterprise integration✅ Azure Functions, serverless architecture, and managed identity✅ Azure Service Bus and event-driven architecture✅ Cloud architecture, reliability, security, and cost optimisation The deployment is the easy part. The operating model is the architecture. Follow the podcast to build a stronger foundation in Azure architecture and make better infrastructure decisions at enterprise scale. #Terraform #Bicep #AzureLandingZones #InfrastructureAsCode #MicrosoftAzure #CloudArchitecture #PlatformEngineering #AzureDevOps #AzureGovernance #AzureArchitect

  2. Oct 16

    Azure Service Bus Prefetch vs Concurrency vs Flow Control Explained: Throughput Tuning, Backpressure, AMQP Credit Model, and Performance Under Load | Azure Counsel

    Microsoft Azure Service Bus is often treated as a simple queueing layer, but under the hood it behaves more like a controlled, adaptive streaming system built on credit-based flow control, buffering, and backpressure management. When throughput breaks in production, it is rarely a “queue issue”—it is almost always a misunderstanding of how Prefetch, Concurrency, and Flow Control interact under load. In this episode, Bhanu (Azure Architect & Developer Advocate) breaks down the internal mechanics that determine how messages move through Azure Service Bus and why systems slow down, saturate, or stabilize depending on configuration choices that most engineers never fully understand. We start with a real production scenario involving Service Bus, Azure Functions, and a downstream SQL database, where throughput degradation is incorrectly attributed to “Service Bus slowness.” In reality, the bottleneck is often downstream processing capacity feeding back into the messaging pipeline. You’ll then explore Prefetch as a buffering mechanism rather than a simple performance toggle. Prefetch pulls messages ahead of time into a local buffer to reduce latency, but misconfiguring it—especially treating it as equivalent to batch size—can cause receiver starvation. This is one of the most common but least understood causes of unstable throughput in production systems. We then break down Concurrency control, which governs how many messages are processed in parallel. Increasing concurrency does not automatically improve throughput. In fact, when combined with slow downstream dependencies such as databases or external APIs, higher concurrency often increases contention, leading to throttling, lock contention, and degraded overall performance. A key part of the discussion focuses on Flow Control in Microsoft Azure Service Bus using AMQP credit-based negotiation. Instead of pushing messages blindly, Service Bus continuously evaluates consumer readiness through credit windows. This ensures that message delivery adapts dynamically to consumer speed, preventing overload conditions before they happen. We then connect this to Backpressure, the most misunderstood concept in distributed messaging systems. Backpressure is not a failure state—it is a protective mechanism. It ensures that when downstream systems slow down, upstream message delivery naturally adjusts rather than collapsing under pressure. Through a tank-and-pump mental model, we visualize how Prefetch buffers messages, how concurrency determines processing throughput, and how flow control continuously regulates delivery speed. This combined system explains why Azure Service Bus often appears “self-stabilizing” under load rather than simply scaling linearly. A major insight from this episode is that Azure Service Bus does not operate as a passive queue. It behaves as an adaptive control system that continuously negotiates throughput based on real-time consumer performance. This is why it rarely breaks under load—but instead degrades gracefully when misconfigured. We also cover what happens when consumers slow down, when prefetch buffers are exhausted, and when no messages are available in the queue. These states are not failures—they are expected behaviors in a backpressure-driven architecture. By the end of this episode, you will be able to move beyond surface-level tuning and develop a mental model of Azure Service Bus as a flow-controlled system rather than a message buffer. This shift is critical for designing stable, high-throughput, event-driven systems. The video also includes a performance tuning demo and GitHub walkthrough available here: https://github.com/Azure-Counsel/ServiceBus/tree/main/PerformanceTuning

  3. Aug 27

    Azure Service Bus Idempotency Mistake: Why Duplicate Detection Fails in Production, Retry Storms, Redis Traps, and Real Distributed System Correctness Patterns | Azure Counsel

    Most developers assume Microsoft Azure Service Bus Duplicate Detection solves idempotency by design. The configuration feels reassuring: enable duplicate detection and the system will automatically prevent repeated processing of the same message. In production systems, that assumption quietly breaks. Nothing fails visibly. No exceptions. No retries. No DLQ spikes. No alerts. Yet the same business operation still executes multiple times—leading to duplicate payments, inconsistent order states, and silent data corruption across downstream services. This is the hidden failure mode of Azure Service Bus Duplicate Detection that only surfaces during production incidents, reconciliation mismatches, or financial audits. In this episode, Bhanu (Azure Architect & Developer Advocate) breaks down why duplicate detection is often misunderstood as a correctness guarantee, when in reality it is only a short-lived broker-level optimization. You’ll learn how systems that appear “idempotent by configuration” still fail under real-world distributed conditions such as retry storms, consumer crashes, lock timeouts, transient database failures, and replay scenarios during recovery workflows. The core issue is architectural: duplicate detection operates only within a bounded time window and only at the broker level. Once that window expires—or when messages are retried through alternate paths—it no longer provides protection. At that point, correctness depends entirely on application-level design. We walk through a real production failure scenario where a payment message is successfully processed, but the downstream database transaction times out. The consumer crashes before confirming completion. The message is retried after the broker’s deduplication window has expired, and the same operation is executed again—resulting in a double charge. No errors are thrown. No system components fail. But the business state is now permanently inconsistent. This is not a messaging failure. It is a distributed consistency failure. You’ll also see why increasing the duplicate detection window does not solve the problem—it only delays failure. In high-throughput or globally distributed systems, replay windows are always shorter than real-world failure recovery timelines. The video then transitions into what real idempotency looks like in production systems. Instead of relying on messaging-layer guarantees, correctness must be enforced at the data layer using patterns such as the Inbox Pattern, transactional idempotency tables, and deterministic state transitions. We also analyze common pitfalls such as using Redis as a standalone idempotency store. While fast and convenient, Redis introduces durability and race-condition risks under failover scenarios, making it unsafe as the sole source of truth for correctness in high-scale systems. By the end of this episode, you’ll understand the fundamental distinction between broker-level duplicate detection and true application-level idempotency. One reduces noise. The other guarantees correctness. This distinction is critical in systems where correctness matters: financial processing, order management, inventory updates, and distributed workflow orchestration. The companion repository includes a full working demo and implementation reference: https://github.com/Azure-Counsel/ServiceBus/tree/main/Idempotency. In enterprise-grade architectures, idempotency is not optional. It is the boundary between reliable systems and silently corrupt ones. This episode is essential for anyone building event-driven systems on Azure who wants to avoid hidden duplication bugs that only appear under load, failure, or recovery conditions. ---------------------------------------------------------------Don’t forget to subscribe for more real-world Azure architecture deep dives and production-ready patterns: ⁠https://www.youtube.com/@azurecounsel

  4. Aug 13

    Azure Service Bus Sessions Are Breaking FIFO: Silent State Corruption, Session Lock Expiry, Idempotency, and Production Fixes for Distributed Messaging Systems | Azure Counsel

    Most developers assume Microsoft Azure Service Bus Sessions guarantee strict FIFO ordering once a session ID is applied. In production, that assumption quietly breaks under load, latency, and lock misalignment. Nothing fails. No exceptions. No retries. No DLQ spikes. Yet the system still processes messages out of order within the same session, silently corrupting state transitions. This is the hidden failure mode of Azure Service Bus Sessions most engineers only discover during audits, reconciliation, or production incidents. In this episode, Bhanu (Azure Architect & Developer Advocate) breaks down how session-based FIFO appears deterministic but becomes probabilistic under real-world constraints. This video walks through how session lock expiry, long-running processing, retries, and external latency combine to break ordering guarantees inside a single Azure Service Bus session. You will see how lock duration is time-based rather than workload-based, leading to overlap where multiple workers process messages from the same session concurrently without errors. Through a live-style scenario, you’ll observe how Worker A processes Message 1, but the session lock expires mid-execution due to external API delays or throttling. Worker B then acquires the same session and processes Message 2 concurrently. Both operations succeed independently, but the final state becomes non-deterministic and out of sequence. We then break down why this matters: silent ordering violations lead to incorrect financial updates, broken workflow state machines, data integrity issues, and audit mismatches. The critical insight is that Azure Service Bus Sessions are not designed for long-running orchestration—they are optimized for short-lived, deterministic message sequencing. To mitigate this, the video introduces three production-grade strategies: session lock renewal using heartbeat patterns, idempotency with optimistic concurrency control (ETags), and workflow decomposition using pipes-and-filters to avoid long-running session locks altogether. This episode also clarifies the distinction between infrastructure failure and logical consistency failure—showing how a system can report successful message processing while still producing incorrect business state due to timing-based concurrency issues in distributed messaging systems. Code samples and a live walkthrough are available in the companion repository: https://github.com/Azure-Counsel/ServiceBus/tree/main/SessionQueueLockExpiry. For more deep dives into Azure Service Bus, Azure Functions, and distributed system reliability patterns, subscribe to Azure Counsel, where we break down production-grade cloud architecture beyond documentation. This is a critical watch for engineers building event-driven systems where Azure Service Bus Sessions, Azure Functions, and distributed workflows are assumed to provide deterministic ordering. In real production environments, timing, retries, and lock expiration introduce subtle race conditions that are not captured in documentation but directly impact system correctness. Don’t forget to subscribe for more real-world Azure architecture deep dives and production-ready patterns: ⁠https://www.youtube.com/@azurecounsel

  5. Jul 30

    Azure Service Bus Dead Letter Queue Self-Healing Pattern | DLQ Recovery, Retry Design & Production Messaging Resilience with Azure Functions

    Most Azure Service Bus implementations focus on how messages enter a Dead Letter Queue (DLQ). Very few explain what happens after that—and this is where production systems quietly fail. A DLQ is not a discard mechanism. It is a failure boundary designed to protect your system from poison messages, transient faults, and downstream instability. But without a recovery strategy, it becomes a backlog of lost orders, missed payments, broken workflows, and silent system degradation that never triggers obvious alerts. In this episode, Bhanu (Azure Architect & Developer Advocate) breaks down a production-grade Azure Service Bus DLQ Self-Healing Pattern designed for real-world distributed systems operating at scale. You’ll go beyond manual DLQ inspection and learn how to build automated recovery pipelines using Azure Functions, intelligent retry orchestration, and controlled reprocessing logic that prevents cascading failures across your architecture. This is not theoretical. It reflects real enterprise scenarios where dashboards remain green while critical business events are silently accumulating inside the DLQ. --------------------------------------------------------------------🧠 Core Problem: Why DLQs Break Production Systems Most failures do not happen at ingestion—they happen at recovery. You’ll understand: Why Azure Service Bus Dead Letter Queues existHow silent failures accumulate without triggering alertsWhy manual DLQ replay does not scale in distributed systemsThe difference between transient failures and poison messagesWhy naive retry logic leads to retry storms and instabilityYou’ll also see common architectural blind spots that result in: Duplicate processing loopsMessage replay amplificationData corruption from uncontrolled retriesOperational overload on engineering teams--------------------------------------------------------------------🚀 Live Production Scenarios You’ll see real-world failures handled end-to-end: ✅ Timeout Failure Recovery Messages fail due to transient dependencies, move into the DLQ, are inspected, and automatically requeued using controlled retry logic. ⚠️ HTTP 429 Throttling Scenario The system detects rate limiting, applies adaptive backoff, and prevents retry storms that could destabilize dependent services. ❌ Poison Message Handling Invalid or malformed payloads are detected immediately and archived, preventing infinite retry loops and wasted compute cycles.-------------------------------------------------------------------- ⚠️ Why This Matters in Production Ignoring DLQ recovery leads to serious operational and business risks: Lost transactions and revenue leakageBroken order processing workflowsSilent data inconsistency across systemsCompliance and audit failuresHidden system degradation at scaleA DLQ is not a dead end—it is a recovery pipeline waiting to be activated.--------------------------------------------------------------------📦 GitHub Repository Production-ready implementation of the DLQ Self-Healing Pattern: https://github.com/Azure-Counsel/ServiceBus/tree/main/DeadLetterAutoHeal Includes: Azure Function remediation workerDLQ processing logicRetry classification engineSafe requeue implementationBlob-based archival strategy--------------------------------------------------------------------🎯 Outcome By the end of this episode, you’ll understand how to transform Azure Service Bus DLQs from passive failure sinks into active recovery pipelines that improve resilience, reduce operational overhead, and prevent silent production failures at scale.--------------------------------------------------------------------Don’t forget to subscribe for more real-world Azure architecture deep dives and production-ready patterns: https://www.youtube.com/@azurecounsel

  6. Jul 16

    Why Cloud Systems Fail at Scale: Distributed Blocking, Shared State Bottlenecks & Event Storms Explained (Fix Architecture Before Real Traffic Breaks It)

    Most cloud systems don’t fail because of obvious bugs. They fail because everything looks correct—until real traffic arrives. Dashboards are green. Services are healthy. Deployments succeed. But under load, the system behaves like it was never designed for scale. This episode breaks down a critical truth: success in isolation does not guarantee behavior at scale. And more importantly—cloud failure is rarely a tooling issue. It’s an architectural one. In the previous discussion, we explored “adoption debt”—how early decisions during cloud migration silently shape long-term system behavior. Now we go one layer deeper into the architecture itself. Because even when adoption is done correctly, systems still collapse under pressure. The reason? Hidden coupling that only emerges under load. ⚠️ The 3 Core Architectural Failure ArchetypesModern systems often rely on service-to-service calls:Service A → Service B → Service C → Database At small scale, this works. At large scale, latency compounds. A 500ms delay downstream doesn’t stay isolated—it propagates backward through every upstream dependency. Threads get blocked. Queues start forming. Timeouts cascade. This isn’t just a slow system. It’s a system waiting on itself. This is distributed blocking—a monolith disguised as microservices. Compute scales horizontally. State does not. Most systems centralize data into shared databases. At low traffic, it works fine. But under high concurrency, contention begins:• Row locks• IOPS saturation• Connection pool exhaustion Now your compute layer scales to hundreds of instances—but they all compete for the same database. Instead of scaling performance, you scale the bottleneck. This is asymmetric scaling—and it quietly limits system throughput long before infrastructure limits are reached. Event-driven architectures solve synchronous blocking—but introduce a different risk. Uncontrolled event propagation. When consumers lag behind:• Retries begin• Events multiply• Load increases beyond original traffic The system doesn’t recover—it amplifies its own failure. This is reactive overload: a system becoming the source of its own collapse. 🧠 The Real Pattern Behind Cloud FailuresIf you step back, all these failures share a common root cause: Tight coupling under pressure. • Time coupling → synchronous dependencies• State coupling → shared databases• Load coupling → event amplification At small scale, these issues remain invisible.At large scale, they become system-defining constraints. Cloud systems don’t fail at design time.They fail at load time. ⚙️ Why This MattersMost engineers learn cloud services in isolation:• Compute• Storage• Networking• Messaging But systems don’t fail in isolation. They fail at the interaction layer—where services communicate, depend, and amplify each other’s behavior. That’s where architecture actually lives. And that’s why scaling infrastructure alone doesn’t solve the problem.You don’t fix scale by adding resources.You fix scale by removing coupling. 👨‍💻 Who This Episode Is For• Cloud Architects designing distributed systems at scale• Backend Engineers working with microservices and event-driven systems• DevOps Engineers troubleshooting latency, retries, and system instability• Teams experiencing performance degradation under real-world traffic 🚀 What You’ll Walk Away With• A clear mental model of why systems collapse under load• The ability to identify hidden coupling in your architecture• A framework to reason about latency, scaling, and failure propagation• Insight into why “healthy systems” still fail in production

  7. Jul 2

    Cloud Adoption Mistakes Explained: Why Lift & Shift, Poor Architecture and Uncontrolled Scaling Create Hidden Cloud Costs and System Complexity Over Time

    Most cloud migrations look successful at first. The system is live. Dashboards are green. Everything appears stable.But then, 3–6 months later, something changes. Costs start rising.Systems slow down.Changes become harder than expected. This isn’t a cloud failure. It’s an adoption failure. In cloud architecture, the biggest risks don’t appear during migration.They accumulate silently after deployment. This has a name: Adoption Debt. Unlike technical debt, adoption debt doesn’t show up immediately.It builds gradually across: • Architecture decisions• Operational patterns• Cost behavior• Platform usage And by the time it becomes visible, your system is already harder to fix than to rebuild. The most common migration strategy—and the most misunderstood. You move an on-prem system into the cloud without redesigning it: • Same monolithic architecture• Same synchronous dependencies• Same database constraints But the environment has changed. You’re now running on-prem architecture with cloud pricing. And because it “works,” teams delay fixing it—allowing inefficiencies to persist in a more expensive environment. Cloud adoption happens at the team level. One team uses serverless.Another uses containers.Another stays on virtual machines. Individually, each choice is valid. But system-wide, this creates fragmentation. You no longer have a unified platform—you have multiple execution models pretending to be one system. When failures happen: The problem isn’t debugging.It’s identifying where the failure belongs. This is not a technical issue—it’s an architectural clarity problem. Cloud makes scaling effortless. But scaling without constraints creates financial chaos. Over time, resources accumulate: • Test environments left running• Temporary workloads never deleted• Over-provisioned compute• Idle services consuming budget Without: • Tagging strategies• Budget controls• Lifecycle policies Cost becomes unpredictable. It’s no longer controlled—it becomes an emergent property of usage. The most critical—and most overlooked failure. Teams adopt modern cloud platforms…But design systems using legacy assumptions: • Tight coupling between services• Shared databases• Synchronous communication patterns At low scale, everything works. At high scale, every dependency becomes a bottleneck. 👉 Key insight:Cloud does not fix architecture problems.It exposes them under load and at speed. These are not isolated mistakes. They are connected symptoms of a deeper issue: Adoption without architectural alignment. When alignment is missing across: • System design• Platform capabilities• Cost behavior• Operational strategy Your system doesn’t fail immediately. It becomes: • Harder to scale• Harder to maintain• Harder to evolve Until rebuilding becomes easier than fixing. • Why cloud migrations fail months after deployment• The real cost of lift-and-shift strategies• How fragmented adoption creates system complexity• Why uncontrolled scaling leads to financial drift• The architectural patterns that break systems at scale• How to identify and avoid adoption debt early • Cloud Architects designing scalable systems• Developers migrating from on-prem to cloud• DevOps Engineers managing cloud cost and operations• Teams experiencing rising cloud costs without clear reasons• Anyone building systems on Azure, AWS, or GCP Most cloud failures are not caused by bad technology. They are caused by misaligned adoption decisions made early—and left unchallenged. Fixing them later is exponentially harder. This episode gives you the mental model to identify those failures before they become irreversible.

  8. Jun 18

    IaaS vs PaaS vs SaaS Explained: The Hidden Responsibility Trap Behind Cloud Costs, Architecture Mistakes & System Ownership in Azure & AWS

    Most engineers assume IaaS, PaaS, and SaaS are just different hosting options. They’re not. They are three fundamentally different responsibility contracts that determine how much of your system you own, maintain, and pay for—often in ways that only become visible after production failures or unexpected cloud bills. In this episode, we break down the real trade-offs behind cloud adoption models and why teams frequently end up with higher costs, slower delivery cycles, and more operational complexity—even after “moving to the cloud.” This is not a definitions video.This is a decision-making framework for real-world cloud architecture. Cloud complexity rarely comes from technology. It comes from misaligned ownership decisions. 🧱 IaaS — Maximum Control, Maximum MaintenanceInfrastructure as a Service gives you full control over virtual machines, operating systems, and runtime environments. But control comes with hidden cost: • OS patching and security updates• Middleware upgrades and compatibility fixes• Scaling scripts and capacity planning• Infrastructure troubleshooting at odd hours In real-world engineering teams, this can consume 30–40% of engineering capacity without producing business features. 👉 Insight: If it’s not deeply specialized infrastructure work, IaaS becomes a maintenance tax. ⚙️ PaaS — Speed, but with Architectural CouplingPlatform as a Service is where most modern cloud systems belong. You deploy code. The platform handles scaling, infrastructure, and runtime management. But the trade-off is subtle and often misunderstood: It’s not lock-in—it’s integration coupling. Over time, systems become tightly bound to: • Event-driven triggers• Managed identity flows• Platform-specific bindings• Native scaling behaviors 🧩 SaaS — The Hidden Cost of Rebuilding What Already ExistsSoftware as a Service is often misunderstood as “limited control.” But the real issue is different: Teams often rebuild existing capabilities instead of adopting them. Examples: • Authentication systems• Notification pipelines• Admin dashboards• Internal tools This leads to months of engineering effort spent recreating commodity software. 👉 Insight: If it’s not your competitive advantage, building it is usually technical debt in disguise. 🧠 The Mental Model That Makes This SimpleThink of it like this: • IaaS = Your own kitchen (you cook, clean, manage everything)• PaaS = Food delivery (you focus on the application, not infrastructure)• SaaS = Restaurant (you just consume the outcome) The mistake most teams make? Building a kitchen… when all they needed was dinner. 🎯 The Decision FrameworkBefore choosing a model, ask: • Is this commodity functionality? → SaaS• Is this core application logic? → PaaS• Is this deep infrastructure control? → IaaS Simple, but precise. 🔥 Why This MattersMost cloud cost overruns, scaling issues, and operational complexity are not caused by poor tools. They are caused by wrong responsibility placement at the architecture layer. Once you choose the wrong model, every downstream decision becomes more expensive: • More operational overhead• Slower delivery cycles• Higher cloud bills• Increased system fragility 🚀 What You’ll Take Away• A clear mental model for IaaS vs PaaS vs SaaS• How responsibility shifts affect cost and complexity• Why PaaS systems become hard to migrate over time• When SaaS prevents unnecessary engineering work• How to avoid over-engineering cloud systems 🎓 About Azure CounselAzure Counsel focuses on real-world cloud architecture beyond tutorials. We explore: • Azure Functions & Serverless systems• Event-driven architecture patterns• API Management & integration design• Cost optimization strategies• Production debugging and scaling lessons If you're building systems that need to scale reliably and cost-effectively, this channel is built for you.

See All (21)

About

Azure Counsel Podcast brings you practical tutorials and deep dives into Microsoft Azure, serverless computing, Event Hubs, IoT, and cloud architecture. Learn how to build scalable, real-world applications using Azure Functions, Service Bus, Cosmos DB, and more. Perfect for developers, architects, and cloud professionals seeking hands-on guidance, automation tips, and workflow optimizations. Watch the full video of each episode on my Youtube channel: https://www.youtube.com/@azurecounsel

Information

  • Creator
    Bhanu Prakash - Azure Counsel
  • Years Active
    2025 - 2026
  • Episodes
    21
  • Rating
    Clean
  • Copyright
    © Bhanu Prakash - Azure Counsel
  • Show Website
    Azure Counsel Podcast