AI-focused innovations in Dataflow
Google created MapReduce more than 20 years ago to solve the scaling problems in data processing that the then young company was running into. The AI era that we are in now demands efficient, large-scale data processing for everything from training frontier models like Gemini by Google DeepMind to powering fully autonomous vehicles like Waymo.
Many aspects of machine learning, including data ingestion, transformation, and feature extraction, rely heavily on processing massive datasets. To meet this astronomical scale required by efforts across Google, we evolved our data platform, Flume, the successor to the original MapReduce, with innovations focused on scalability, efficiency, and a better developer experience. And many of those innovations are available as part of Dataflow, our fully managed batch and streaming platform built on the same core technology Google uses to power its most demanding internal workloads.. In this blog, we provide an overview...
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