You deployed your Azure Function…
But now your logs are missing, out of order, or so vague they’re useless.
Worse — your Application Insights bill is exploding while you’re still debugging blind in production.

Sound familiar? 😅

In this episode, Bhanu from Azure Counsel breaks down the exact logging architecture used to build traceable, distributed Azure Functions systems — without burning money on telemetry.

This is not about Console.WriteLine.
It’s about structured logging, correlation IDs, and cost-aware telemetry design for real production workloads.

⚠️ Critical for 2026 and Beyond
With the November 10, 2026 retirement of the Azure Functions In-Process model, your logging strategy must change.
This episode focuses on ILogger + dependency injection for the Isolated Worker model and how logging behavior differs from the old runtime.

🎯 What You’ll Learn:

• How to correctly configure APPLICATIONINSIGHTS_CONNECTION_STRING for local and production

• Why most logs disappear or arrive out of order

• How to tune log levels globally and per function to suppress noise

• How Azure drops telemetry when sampling is misconfigured

• How maxTelemetryItemsPerSecond can cut 90% of your ingestion cost

• When to use LogInformation, LogWarning, and LogCritical

• How to centralize logging using a helper class

• How to inject Correlation IDs for distributed tracing

• Why your error logging strategy defines production stability

🧠 Key Takeaways:

• Pay for insight, not noise

• Preserve statistical accuracy while reducing cost

• Prevent silent outages caused by sampling misconfiguration

• Build traceable request flows across microservices

• Stop debugging blind in production

👨‍💻 Who This Episode Is For:

• Cloud Architects designing high-volume telemetry systems

• Senior Developers building Azure Functions with C#

• DevOps Engineers managing Log Analytics cost and alerts

• Teams migrating to .NET 8/10 Isolated Worker

• Engineers tired of runaway Application Insights bills

🔧 Technical Focus Areas:

• Azure Functions v4 & Isolated Worker model

• Application Insights & Log Analytics

• host.json sampling and aggregator settings

• Structured logging with ILogger

• Distributed tracing & Correlation IDs

If your Azure Functions have ever:
• lost logs
• flooded Application Insights
• missed exceptions
• or cost more to monitor than to run

This episode gives you the logging blueprint to fix it permanently.

🎥 Watch the full walkthrough on YouTube:

https://youtu.be/nDR_LwzS3U8

Most developers assume Azure Functions “just scale automatically.”
The truth? One wrong configuration setting can silently flood your telemetry, expose secrets, overload downstream systems, or trigger a midnight production outage.

In this episode, Bhanu from Azure Counsel breaks down the 5 most dangerous configuration mistakes that cripple real-world Azure Functions deployments — and shows you how to fix them using production-grade patterns.

This is not a beginner tutorial. It’s a practical guide for engineers who care about security, observability, concurrency control, and cost stability.

⚠️ Important for 2026 and beyond
With the November 10, 2026 retirement of the Azure Functions In-Process model, migrating to the Isolated Worker model is no longer optional. This episode focuses on the configuration patterns you must adopt to survive that transition safely.

🎯 What You’ll Learn:

• Why default Azure Functions settings are unsafe for production

• How misconfigured host.json can destroy performance and inflate Application Insights costs

• Why hardcoding connection strings is a security risk

• The correct way to use Managed Identity + Key Vault references

• When to use static vs. dynamic concurrency

• How to tune Service Bus and Event Hub triggers without melting downstream services

• How to prevent HTTP triggers from causing 429 storms

• Why logging to disk (fileLoggingMode) hurts performance

• How to design layered configuration for dev vs. prod

🧠 Key Takeaways:

• Control telemetry volume using metrics.aggregator tuning

• Implement structured logging instead of file-based logging

• Regulate parallelism using trigger-specific limits

• Protect backend systems with concurrency governance

• Build resilient deployments with feature flags and slots

👨‍💻 Who This Episode Is For:

• Cloud Architects designing enterprise serverless systems

• Senior Developers preparing for AZ-204 or AZ-305

• DevOps Engineers optimizing latency and cost

• Teams migrating legacy In-Process Functions to .NET Isolated Worker

• Engineers tired of “it worked in dev” failures

🔧 Technical Focus Areas:

• Azure Functions v4 (Isolated Worker model)

• Service Bus & Event Hub trigger tuning

• Cosmos DB Change Feed lease optimization

• Application Insights sampling & logging strategy

• Infrastructure as Code (Bicep & Terraform)

If your Azure Functions have ever:
• suddenly slowed down
• blown up your telemetry bill
• exposed secrets
• overwhelmed downstream systems
• or failed only in production

This episode gives you the mental model and configuration blueprint to stop those failures before they happen.

🎥 Watch the full visual walkthrough on YouTube:
https://youtu.be/1Y4J6YSqy08

Confused about Azure messaging? In this episode, Azure Architect Bhanu Prakash explains Event Grid vs Event Hub vs Service Bus, helping developers and architects choose the right Azure service for event-driven and serverless systems.

Learn when to use Event Grid for events, Event Hubs for telemetry and streaming, and Service Bus for reliable enterprise messaging — with real-world Azure architecture examples in under 5 minutes.

Event Grid, Event Hub, and Service Bus are some of the most misunderstood services in Azure — and treating them as interchangeable is a common reason event-driven systems fail in production.

In this short, focused episode, Bhanu Prakash (Azure Architect & Developer Advocate) explains Azure messaging the right way, using a clear mental model that helps developers and architects confidently choose between Event Grid, Event Hubs, and Service Bus.

You’ll learn:
• The real difference between events vs messages — and why it matters
• When to use Event Grid for reactive notifications and integrations
• When Event Hubs is the right choice for telemetry and streaming data
• When Service Bus is essential for reliable, ordered, enterprise messaging
• How throughput, ordering, retries, and guarantees affect architecture decisions

This episode cuts through Azure documentation noise and focuses on real-world design trade-offs, not demos or theory. Perfect if you’re building APIs, serverless systems, IoT pipelines, or event-driven architectures on Azure.

If you’ve ever dealt with lost messages, broken ordering, scaling issues, or the wrong messaging choice slowing your system down — this 5-minute episode will reset how you think about Azure messaging.