Big Data Analytics: Tools, Techniques, and Industrial Applications
Keywords:
Big Data Analytics (BDA), Internet of Things (IoT), cloud computing, Descriptive Analytics, Predictive AnalyticsAbstract
Big Data Analytics (BDA) has turned out to be a paradigm shift in the derivation of useful insights with regard to large-scale, complicated and fast-evolving datasets produced in industries. The review paper provides a detailed discussion of the characteristics, tools, techniques, challenges and industrial application of big data analytics. It explains the basic idea of big data with the 7Vs model and introduces such significant platforms as Hadoop, Apache Spark, Kafka, Flink, Hive, and Presto that allow storing data and performing large-scale processing on a distributed basis. The paper also looks into methods of analysis such as descriptive, diagnostic, predictive, prescriptive, and real-time analytics, as well as integration of machine learning and artificial intelligence. A systematic literature review of recent articles determines trends, challenges and opportunities of research in segments of finance, energy, telecommunication, agriculture, health care and supply chain management. The results highlight the increased value of scalable, safe, and intelligent data analytics solutions to data-driven industrial change.
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