Introduction
Most developers can make a single Microsoft Dataverse API call that works. Production systems demand more: every request must succeed reliably at scale, respect throttling limits, and use the right API surface for the job. Get these decisions wrong, and an otherwise healthy integration starts failing unpredictably under real-world load.
Consider Contoso, Ltd., a company that built a custom equipment-provisioning system on Dataverse to manage laptops, monitors, and other hardware. The solution tracks employees in a contoso_employee table, available hardware in contoso_device, replacement requests in contoso_request, and each employee's refresh eligibility in contoso_allowance.
When an employee submits a request, the system checks it against their allowance, while an external inventory service continuously updates device availability. A coordinator then tracks each request through approval and fulfillment. Keeping this flow reliable means the underlying API calls have to hold up under real usage, not just in a quick test.
In this module, you explore how to perform operations against Dataverse using the Web API, and how the Organization service offers a strongly typed alternative for .NET developers. You learn how Dataverse enforces API limits to protect the platform, and how to design retry policies that keep your integrations resilient when you reach those limits.
You also explore how the Dataverse SDKs for .NET and Python simplify common operations while still relying on the same underlying API behavior. Throughout, the Contoso equipment-provisioning scenario grounds each concept in a concrete, recognizable integration challenge.
By the end of this module, you're able to perform reliable Dataverse API operations. You do this by choosing the right API surface for a given scenario and applying a resilience strategy that handles throttling gracefully.