Serving multi-gigabyte datasets through synchronous HTTP APIs creates a dangerous memory bottleneck. Traditional buffering models in frameworks like Flask or Django load entire result sets into RAM, risking Out-Of-Memory kills and blocking worker threads. For payloads between 100MB and several gigabytes, this approach is architecturally fragile.

The solution is a shift from buffering to streaming using Python generators. Generators enable lazy evaluation, decoupling server memory footprint from dataset size. This transforms response generation from an O(N) memory operation to an O(1) process, allowing a single container to serve massive exports without degradation. It also dramatically improves Time-to-First-Byte.

Production-grade streaming requires careful orchestration. Engineers must use SQLAlchemy's `yield_per` for database batching to prevent ORM memory spikes. For serialization, high-performance libraries like orjson are critical, as they output bytes directly, avoiding costly string allocations. Finally, NDJSON (Newline Delimited JSON) is the superior protocol, enabling resilient, line-by-line client parsing versus fragile JSON arrays.