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# Architecting Scalable AI Data Ingestion Pipelines for Modern Web Applications

**[SitePoint](https://daily.dev/sources/sitepoint)** · 4 min read · 0 upvotes · 0 comments

## Summary

Covers architectural patterns for building scalable AI data ingestion pipelines that feed web scraping data into RAG systems and LLM applications. Recommends decoupling data extraction from user-facing servers using asynchronous job queues (Node.js with BullMQ), offloading headless browser rendering and anti-bot mitigation to third-party APIs, cleaning and chunking raw HTML for embeddings, and applying fault-tolerance patterns like exponential backoff, caching, and circuit breakers. Also frames the work through an agile development lens, breaking ingestion pipeline work into sprints.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.sitepoint.com/architecting-scalable-ai-data-ingestion-pipelines-for-modern-web-applications>

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---

Tags: [#nodejs](https://daily.dev/tags/nodejs), [#data-engineering](https://daily.dev/tags/data-engineering), [#rag](https://daily.dev/tags/rag), [#redis](https://daily.dev/tags/redis), [#crawling](https://daily.dev/tags/crawling)

[View this post on daily.dev](https://daily.dev/posts/architecting-scalable-ai-data-ingestion-pipelines-for-modern-web-applications-trjjggrdd)

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