Essential Integration Patterns for Dynamics 365 Using Azure Logic Apps

If you’ve worked on Dynamics 365 CRM projects, you know integration isn’t optional—it’s essential. Whether you’re connecting CRM with a legacy ERP, a cloud-based marketing tool, or a SharePoint document library, the way you architect your integrations can make or break performance and maintainability. 

Azure Logic Apps makes this easier with its low code interface but using the right pattern matters. In this post, I’ll Walk through seven integration patterns I’ve seen in real projects, explain where they work best, and share some lessons from the field. 

Whether you’re building real-time syncs, scheduled data pulls, or hybrid workflows using Azure functions, these patterns will help you design cleaner, smarter solutions. 

A Common Real-World Scenario

Let’s say you’re asked to sync Project Tasks from Dynamics 365 to an external project management system. The sync needs to be quick, reliable, and avoid sending duplicate data. 

You might wonder: 

• >Should I use a scheduled Logic App? 

• >Can I push data in real time? 

• >What if the external system is down? 

Without a clear integration pattern, you might end up with brittle flows that break silently or overload your system. 

Key Integration Patterns (With Real Use Cases) 

1. Request-Response Pattern 

What it is: A Logic App that waits for a request (usually via HTTP), processes it, and sends back a response. 

Use Case: You’re building a web or mobile app that pulls data from CRM in real time—like showing a customer’s recent orders. 

How it works: 

• -Trigger: HTTP request 

• -Action: Dataverse “Get row by ID” 

• -Response: Send structured JSON back 

Why use it: 

• -Perfect for chatbots, portals, or validation forms 

• -Fast and reliable when you need immediate results 

Key Considerations: 

• -Response timeout (especially in Consumption tier) 

• -Should be stateless to avoid data issues 

2. Fire-and-Forget Pattern

What it is: CRM pushes data to a Logic App when something happens. The Logic App does the work—but no one waits for confirmation. 

Use Case: When a case is closed in CRM, you archive the data to SQL or notify another system via email. 

How it works: 

• Trigger: “When a row is modified” in Dataverse 

• Actions: Insert into Azure SQL or send a webhook 

Why use it: 

Keep users moving—no delays. 

Great for logging, alerts, or downstream updates 

Key Considerations: 

Silent failures—make sure you’re logging errors or using retries 

3. Scheduled Sync (Polling)

What it is: A Logic App that runs on a fixed schedule and pulls new/updated records using filters. 

Use Case: Every 30 minutes, sync new Opportunities from CRM to SAP. 

How it works: 

• -Trigger: Recurrence (e.g., every 30 mins) 

• -Action: Dataverse “List Rows” with $filter like modifiedon gt ‘lastRun’ 

• -Use checkpointing to avoid duplicates 

Why use it: 

• -Ideal when systems don’t support webhooks 

• -Easier to manage batch jobs 

Key Considerations: 

• -Can waste resources if there’s no new data 

• -Make sure you’re filtering efficiently 

4. Event-Driven Pattern (Webhooks) 

What it is: CRM triggers a webhook (HTTP call) when something happens. A Logic App or Azure Function listens and acts. 

Use Case: When a Project Task is updated, push that data to another system like MS Project or Jira. 

How it works: 

• -Plugin in CRM sends HTTP POST to Azure Function 

• -Function forwards data to Logic App for processing 

Why use it: 

• -Real-time updates with minimal delay 

• -Better than polling when performance matters 

Key Considerations: 

• -Make sure your webhook is secure (use client secrets or managed identity) 

• -Handle retries for transient failures 

5. Queue-Based Pattern 

What it is: Messages are pushed to a queue (like Azure Service Bus), and Logic Apps process them asynchronously. 

Use Case: CRM pushes lead data to a queue, and Logic Apps handle them one by one to update different downstream systems (email marketing, analytics, etc.) 

How it works: 

• -Plugin sends message to Service Bus 

• -Logic App triggers on new messages and processes them 

Why use it: 

• -Scales well under load 

• -Lets you retry or dead-letter failed messages 

Key Considerations: 

• -Set TTL and DLQ settings properly 

• -Watch for message order and duplication 

6. Blob-Driven Pattern (File-Based Integration) 

What it is: Logic App watches a Blob container or SFTP location for new files (CSV, Excel), parses them, and updates CRM. 

Use Case: An external system sends daily contact updates via CSV to a storage account. Logic App reads and applies updates to CRM. 

How it works: 

• -Trigger: Blob “When file is created” 

• -Actions: Parse Excel or CSV → Upsert to Dataverse 

Why use it: 

• -Great for systems without APIs 

• -Easy to migrate data or handle batch updates 

Key Considerations: 

• -Handle duplicates, malformed files, and large file sizes 

• -Secure your storage access with managed identity 

7. Hybrid Pattern (Logic Apps + Azure Functions) 

What it is: Logic App does the orchestration, while Azure Function handles complex logic that’s hard to do with built-in connectors. 

Use Case: You need to calculate dynamic pricing or apply business rules before pushing data to ERP. 

How it works: 

• -Trigger: Any (HTTP, Dataverse, Recurrence) 

• -Mid-step: Call Function App with input JSON 

• -Function returns calculated values → Logic App continues 

Why use it: 

• Reuse logic across workflows 

• Keeps Logic Apps simple and readable 

Key Considerations: 

• -Monitor Function health and failures 

• -Version control is critical for updates 

Implementation Tips & Best Practices 

Tools & Technologies Used

• -Azure Logic Apps (Consumption & Standard) 

• -Azure Service Bus 

• -Azure Blob Storage 

• -Azure Functions (C#) 

• -Microsoft Dataverse (CRM) 

• -Plugins + Webhooks 

• -Power Platform Admin Center 

Common Architectures

You’ll often see combinations of these patterns in real-world systems. For example: 

• –Webhook → Function → Logic App for real-time, rule-based sync 

• –Scheduled Sync → Blob checkpoint → API for incremental batch loads 

• –Dataverse Trigger → Service Bus → Multiple Logic Apps for decoupled fan-out 

To conclude, integration isn’t just about wiring up connectors, it’s about designing flows that are reliable, scalable, and easy to maintain. 

These seven patterns are ones I’ve personally used (and reused!) across projects. Pick the right one for your scenario, and you’ll save yourself and your team countless hours in debugging and rework. 

I hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.