Building a Truly Scalable Multimodal Data Pipeline: A Streaming-First View
Most “Scalable” Multimodal Pipelines Don’t Survive Foundation-Model Scale A lot of multimodal pipelines claim to scale. In practice, they often depend on at least one of the following: global shuffles (groupBy/join/repartition), materializing massive intermediate datasets, centralized coordination that becomes a bottleneck, or brittle recovery logic (rerun-the-world on failure). That works for demos. It breaks at foundation-model scale. This series is about a different design point: A streaming-first multimodal pipeline that scales linearly with data and hardware — with no global shuffle, and resumable at partition granularity. ...
