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- W4254559915 abstract "This chapter focuses on the challenges using traditional methodologies of data and analytics and how modern technologies such as in-database, in-memory processing, and open source technologies (Hadoop, Python, R, and Spark) are ideal for overcoming the challenges. In-database processing refers to the seamless integration of advanced analytics into the data platform functionality. Many organizations in financial, retail, and even government leverage this technology because it offers significant performance improvements over the traditional methods. In-memory processing is becoming the new or next-generation high-performance computing. In-memory processing makes access and analysis of large data volumes possible at incredibly fast speed and yields a higher return on investment. The Hadoop Distributed File System, which is the data storage component of the open source Apache Hadoop project, is ideal for collecting the unstructured data sources. Spark is commonly used for analyzing big data and has a powerful engine for advanced analytics for machine learning." @default.
- W4254559915 created "2022-05-12" @default.
- W4254559915 date "2020-03-23" @default.
- W4254559915 modified "2023-10-18" @default.
- W4254559915 title "Technology" @default.
- W4254559915 doi "https://doi.org/10.1002/9781119613596.ch4" @default.
- W4254559915 hasPublicationYear "2020" @default.
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