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A paradigm shifts in the process of gaining insight from intricate, massive datasets across industries is represented by big data analytics, or BDA. Core methods (Hadoop, Spark, and Python libraries), applications (healthcare diagnostics that achieve 40 % efficiency gains, smart cities, and financial modeling), and persistent problems (data privacy, energy efficiency, and talent gaps) are all deliberately examined in this review. As game-changing solutions, emerging trends like edge AI, quantum analytics, and federated learning are highlighted. This paper gives researchers and practitioners a thorough road map for navigating an evolving BDA landscape while addressing ethical and scalability concerns by combining recent advancements (2020–2024) with practical limitations.

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Big Data Analytics in the AI Era: A Systematic Review of Frameworks, Challenges, and Future Directions

  • 会议论文

  • 发表日期：

    2026年

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  • Barkha Yadav,
  • Ritish Bansal,
  • Ashima Mehta

摘要

A paradigm shifts in the process of gaining insight from intricate, massive datasets across industries is represented by big data analytics, or BDA. Core methods (Hadoop, Spark, and Python libraries), applications (healthcare diagnostics that achieve 40 % efficiency gains, smart cities, and financial modeling), and persistent problems (data privacy, energy efficiency, and talent gaps) are all deliberately examined in this review. As game-changing solutions, emerging trends like edge AI, quantum analytics, and federated learning are highlighted. This paper gives researchers and practitioners a thorough road map for navigating an evolving BDA landscape while addressing ethical and scalability concerns by combining recent advancements (2020–2024) with practical limitations.