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This article describes a common Microsoft Graph integration pattern for a business scenario that requires adding custom enterprise data to the Microsoft Search and Microsoft 365 Copilot experiences. The custom data is ingested into unstructured Microsoft 365 storage and added to various search indexes.
This non-interactive scenario has the following requirements:
The best option for this scenario is to use Microsoft Graph connectors. The following diagram shows the architecture for this solution.
The architecture for this solution includes the following components:
https://graph.microsoft.com
.The following considerations support the use of this integration pattern:
Availability: The client app periodically sends data via Microsoft Graph APIs. The non-interactive client app makes requests and uploads the data at a frequency controlled by the client environment and limited by Microsoft Graph connectors throttling.
Latency: The client app uses the synchronous Microsoft Graph connectors APIs and expects a response, but some latency can occur depending on the network conditions and the load on the Microsoft Graph service.
Scalability: The client app is limited to 30 connections with no more than 50,000,000 items per tenant, so scalability is limited. If the data volume is large, synchronous processing might become a challenge and a roadblock.
Solution complexity: This solution can use connectors built by independent software vendors (ISVs), but if it accesses Microsoft Graph SDKs or Microsoft Graph APIs, it needs to serialize custom data into the required format for connectors. This provides flexibility but also increases the complexity. This solution can therefore range from low to medium complexity.
Events
Mar 17, 11 PM - Mar 21, 11 PM
Join the meetup series to build scalable AI solutions based on real-world use cases with fellow developers and experts.
Register now