17 nəticə — yol xəritələri, maaş bələdçiləri, sertifikatlar və iş bazarı təhlili.
When I built BirJob's scraping pipeline, I faced a classic data engineering decision. We scrape 80+ job sites daily, producing roughly 15,000 job listings per run. Each listing needs deduplication, normalization, categorization, and enrichment before it's visible to users. The naive approach — scrape everything, process synchronously, write to the database — took 4 hours and crashed regularly when a single source timed out. After redesigning around a proper pipeline architecture, the same workload processes in 35 minutes with zero crashes. The architecture choice was the difference between a fragile script and a reliable system.
A couple of years ago, I was debugging a checkout flow that worked perfectly in development but crumbled under real traffic. Orders were duplicated. Inventory went negative. The payment gateway timed out, but the order still went through. I sat in front of my screen at 2 AM, staring at logs from three different services, and realized: I was building a distributed system, and I had no idea what I was doing.
Three years ago, I joined a startup as the third engineer. The codebase was 18 months old, built under extreme time pressure, and it showed. A single feature — adding a filter to the job search page — took 11 days. Not because the feature was complex, but because the search module had accumulated so much technical debt that every change required understanding 14 files across 3 services, running a 40-minute test suite that failed intermittently, and manually testing 6 edge cases that had no automated coverage. The team knew the debt existed. What they didn't have was a framework for deciding what to fix, when to fix it, and how to justify the investment to stakeholders.
When I launched BirJob.com — a job aggregator for Azerbaijan that pulls listings from roughly 80+ sources across the local market — I set up Google Analytics like everyone does. GA4, the tracking snippet, the whole thing. And for a while I convinced myself that sessions, bounce rates, and page views were telling me something useful.
Last year, we shipped a Next.js app to production with zero monitoring. No error tracking, no performance metrics, no log aggregation. It was a job aggregator processing 50,000+ scraping operations daily, serving 200,000+ page views monthly. When things broke — and they broke often — we found out from users. Sometimes days later. The fix-it-when-it-breaks approach cost us an estimated 15% of daily active users before we instrumented everything.