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Build a data loader that streams training data from S3/GCS with on-the-fly transforms. What are the components and failure modes?

The dataset is too big to fit on disk, so you stream it. The components that keep the GPU fed, the throughput math that says whether you'll be I/O-bound, and the failure handling that makes a multi-day run resumable.

Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

The dataset is too big to fit on disk, so you stream it. The components that keep the GPU fed, the throughput math that says whether you'll be I/O-bound, and the failure handling that makes a multi-day run resumable.

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FEDITOR'S NOTE

The screen is whether the candidate keeps the GPU fed: streaming from object storage means prefetch, parallel range-GETs, sharding across workers, and a format that supports sequential reads (WebDataset tar shards / Parquet), not random per-sample GETs. Strong candidates state the throughput target (GPU consumption rate vs network bandwidth) and design backpressure and resumability for a multi-day run. Watch for the candidate who does one synchronous GET per sample and wonders why the GPU sits at 30% utilization.

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