5 Production Stacks for Live Data Ingestion at Scale (Without Getting Blocked)
The article discusses five production stacks for live data ingestion, emphasizing the importance of choosing the right stack based on specific needs. It highlights that many teams over-engineer their data ingestion processes, often using complex tools prematurely. The guide provides insights into simpler solutions that can effectively handle various failure modes without unnecessary complexity.
- ▪Most teams over-engineer data ingestion, using tools like Kafka before reaching their first rate limit.
- ▪The article presents five production-tested stacks for live data ingestion, ranging from minimal fetch + cron to more complex solutions.
- ▪Choosing the right ingestion stack involves selecting the one with the fewest moving parts that still addresses your specific failure mode.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2949644) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Prithwish Nath Posted on May 19 • Originally published at Medium 5 Production Stacks for Live Data Ingestion at Scale (Without Getting Blocked) #webdev #programming #ai #javascript TL;DR: Most teams over-engineer data ingestion. They use Kafka before they’ve hit their first rate limit, or Playwright before they’ve checked the network tab.
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