This chapter investigates the integration of Natural Language Processing (NLP) within the context of big data, emphasizing how massive, diverse, and rapidly evolving text corpora are reshaping the foundations and applications of language technologies. It begins by outlining the historical development, conceptual foundations, and methodological core of NLP, while identifying emerging challenges such as unstructured data representation, noise in large-scale corpora, knowledge sparsity, and the demand for real-time processing. The chapter provides a comprehensive review of essential NLP resources, including linguistic corpora, language knowledge bases, and knowledge graphs, which form the infrastructure for data-driven language analysis. It then delves into key computational techniques such as named entity recognition, relationship extraction, sentiment analysis, machine translation, text summarization, and question answering, all within the context of big data environments. Real-world application scenarios—including intelligent dialogue systems, translation engines, and public opinion monitoring platforms—are examined to illustrate the practical value of combining NLP and big data analytics.