Google’s Dataflow innovations for large-scale AI pipelines

For two decades, Google has been scaling its internal data processing platform, Flume, to handle workloads that now include training models like Gemini and powering Waymo’s autonomous vehicle pipelines.

The article exposes the tension between the astronomical scaling demands of modern AI and the legacy batch-processing assumptions baked into systems like the original MapReduce.

As ML pipelines ingest, transform, and extract features from massive datasets, the old approaches to sharding, scheduling, and resource allocation break down, especially when accelerators like TPUs are in play.