From datapro.news titled "How Large Behaviour Models Are Rewriting Data Engineering Pipelines," explores the transformative impact of Large Behaviour Models (LBMs) on data engineering practices. It highlights a significant shift from language-based AI to embodied, action-oriented intelligence, where machines learn from real-world behaviour and physical interactions. The article explains how traditional batch-oriented data pipelines are becoming obsolete, replaced by real-time, continuous data streams originating from various sensors and human teleoperation. This necessitates new engineering workflows focused on low-latency ingestion, complex temporal and spatial data transformation, and advanced behavioural data curation. Finally, it addresses the critical ethical and technical risks associated with LBMs, such as bias-induced physical harm, bodily privacy concerns, and economic concentration, offering strategic recommendations for data engineers to adapt and lead in this evolving field.

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