HeyJaredAzure Counsel Podcast
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.
- Indexed episodes, last 90 days
- 4
- Latest publication
- Aug 27, 2026
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- Earliest in this view
- Jul 16, 2026
Latest episodes
Azure Service Bus Idempotency Mistake: Why Duplicate Detection Fails in Production, Retry Storms, Redis Traps, and Real Distributed System Correctness Patterns | Azure Counsel (opens the original)
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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, an
Azure Service Bus Sessions Are Breaking FIFO: Silent State Corruption, Session Lock Expiry, Idempotency, and Production Fixes for Distributed Messaging Systems | Azure Counsel (opens the original)
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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, reconcili
Azure Service Bus Dead Letter Queue Self-Healing Pattern | DLQ Recovery, Retry Design & Production Messaging Resilience with Azure Functions (opens the original)
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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
Why Cloud Systems Fail at Scale: Distributed Blocking, Shared State Bottlenecks & Event Storms Explained (Fix Architecture Before Real Traffic Breaks It) (opens the original)
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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 “adop
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