Aggregating Data from Multiple APIs: Patterns and Pitfalls

Introduction Modern applications rarely exist in isolation. A typical dashboard might pull user data from your authentication provider, metrics from your analytics platform, inventory from your ERP system, and notifications from a third-party messaging service. Each API has its own quirks: different authentication schemes, inconsistent response formats, varying rate limits, and unpredictable availability windows. This is the multi-API aggregation challenge : how do you combine data from disparate sources into a unified, reliable response for your application? The naive approach of sequential HTTP calls quickly falls apart. One slow API blocks the entire response. One failed service crashes your page. Rate limits get exhausted because you're making redundant requests. In this article, we'll explore battle-tested patterns for aggregating data from multiple APIs using Python's async capabilities. We'll cover concurrent fetching, rate limiting, response normalization, caching strategies, a