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Auto-Analytics Pipeline: Integrating Big Data Engineering and AI in Vehicle Production provides a comprehensive roadmap for modernizing automotive manufacturing through data-driven intelligence. Beginning with the business case for auto-analytics, the book establishes why integrated data pipelines are critical to competitiveness. It explores data foundations on the shop floor, followed by scalable architectures for ingestion, integration, and lakehouse storage tailored to manufacturing contexts. Real-time event handling, feature engineering, and advanced modeling are presented as the core enablers of predictive quality, yield optimization, and asset health monitoring. Specialized chapters cover computer vision for body and paint shops, EV battery analytics, and supply chain intelligence. The text emphasizes prescriptive analytics for decision automation, while MLOps practices ensure robust deployment and monitoring of AI models. It also highlights governance pillars such as data quality, observability, security, privacy, and compliance.