Solving Real-World Data Fetching Challenges with Next.js and TanStack Query: A Pragmatic Case Study
summary
A fintech case study shows replacing five parallel partner-API calls with a single Next.js API handler acting as a BFF aggregation proxy, moving mapping and retries to the server and returning a 666 byte payload — a 99.9% payload reduction — to the client. Client-side state and retries become TanStack Query concerns, enabling in-memory caching, server prefetch and dehydration, and fewer re-renders while the proxy handles request orchestration and resilience.
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chapters (26)
- 0:00Introduction
- 1:09Page load speed and revenue
- 4:00Problem and solutions
- 9:12Network congestion windows
- 11:17Next.js and TanStack Query
- 13:03API types and challenges
- 15:52User birthday chart case study
- 17:39Initial architecture flaws
- 20:29First demo payload issues
- 23:16Backend for Frontend pattern
- 24:40BFF and reduced payloads
- 26:48BFF benefits
- 27:51Second demo optimized
- 28:33Bandwidth impact on performance
- 30:18Cloud ingress vs egress costs
- 32:04TanStack Query for resilience
- 34:12TanStack Query advantages
- 35:36Query criticality and prioritization
- 36:20Third demo server-side fetching
- 37:42Why pragmatic solutions
- 39:17Real user time and conversions
- 40:24Q&A backend caching
- 41:55Q&A client vs proxy caching
- 42:15Q&A BFF deployment
- 43:00Q&A proxy vs frontend caching
- 44:23Closing remarks
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