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Why ETL and AI Aren’t Rivals, but Partners in Data’s Future
- Large models and ETL (Extract, Transform, Load) processes can coexist, and will not replace each other.
- Despite the excellent performance of large models in many areas, ETL remains an efficient, deterministic and transparent tool for data processing.
- Large models' efficiency depends on high-quality data and hardware demands.
- ETL is highly transparent, with every data handling step documented and auditable, ensuring compliance with corporate and industry standards.
- Future ETL tools will embed AI capabilities, merging traditional strengths with modern intelligence.
- As ETL and large model functionalities become increasingly intertwined, data processing is evolving into a multifunctional, collaborative platform.
- The foundation of data processing is shifting from CPU-centric systems to a collaborative approach involving CPUs and GPUs.
- AI-enhanced ETL represents a transformative leap from traditional ETL, offering embedding generation, LLM-based knowledge extraction, unstructured data processing, and dynamic rule generation.
- Tools like Apache Seatunnel illustrate how modern data processing has evolved into an AI+Big Data full-stack collaboration system, becoming central to enterprise AI and data strategies.
- The convergence of large models and ETL will propel data processing into a new era of intelligence, standardization, and openness, becoming a core engine for the future of data-driven enterprises.
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