Восемь API за день: как я собрал тренд-систему в production

Building a Trend Analyzer: When One Data Source Isn’t Enough

The task was deceptively simple: make the trend-analysis project smarter by feeding it data from eight different sources instead of relying on a single feed. But as anyone who’s integrated third-party APIs knows, “simple” and “reality” rarely align.

The project needed to aggregate signals from wildly different platforms—Reddit discussions, YouTube engagement metrics, academic papers from PubMed, tech discussions on Stack Overflow. Each had its own rate limits, authentication quirks, and data structures. The goal was clear: normalize everything into a unified scoring system that could identify emerging trends across social media, news, search behavior, and academic research simultaneously.

First thing I did was architect the config layer. Each source needed its own configuration model with explicit rate limits and timeout values. Reddit has rate limits. So does NewsAPI. YouTube is auth-gated. Rather than hardcoding these details, I created source-specific adapters with proper error handling and health checks. This meant building async pipelines that could fail gracefully—if one source goes down, the others keep running.

The real challenge emerged when normalizing signals. Reddit’s “upvotes” meant something completely different from YouTube’s “views” or a PubMed paper’s citation count. I had to establish baselines and category weights—treating social signals differently from academic ones. Google Trends returned a normalized 0-100 interest score, which was convenient. Stack Overflow provided raw view counts that needed scaling. The scoring system extracted 18+ new signals from metadata and weighted them per category, all normalized to 1.0 per category for consistency.

Unexpectedly, the health checks became the trickiest part. Of the 13 adapters registered, only 10 passed initial verification—three were blocked by authentication gates. This meant building a system that didn’t fail on partial data. The unit tests (22 of them) and end-to-end tests had to account for auth failures, rate limiting, and network timeouts.

Here’s something interesting about APIs in production: they’re rarely as documented as they claim to be. Rate limit headers vary by service. Error responses are inconsistent. Some endpoints return data in milliseconds, others take seconds. Building an aggregator taught me that async patterns (like Python’s asyncio) aren’t luxury—they’re necessity. Without proper async/await patterns, waiting for eight sequential API calls would be glacial.

By the end, the pipeline could pull trend signals from Reddit discussions, YouTube engagement, Google search interest, academic research, tech community conversations, and product launches simultaneously. The baselines and category weights ensured that a viral Reddit post didn’t drown out sustained academic interest in the same topic.

The system proved that diversity in data sources creates smarter analysis. No single platform tells the whole story of a trend.

😄 “Why did the API go to therapy? Because it had too many issues and couldn’t handle the requests.”