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- W1666440859 abstract "This chapter investigates using batch processing to mine and learn from larger amounts of data instead of streaming data. It covers using Hadoop, Sqoop, and Pig for large-scale batch processing; these enable large data sets to be processed with ease. The chapter also discusses more traditional methods of creating programs to run batch processes on data. It mentions the term Big Data that is increasingly a topic in business today. Key points needed to consider when working with real-time data include volume and frequency, quantity of data, and the processes to be deployed. The chapter details the different approaches to batch data processing. It presents four complete scenarios that walk you through the process of mining batched data from start to finish. A small amount of exposure to Hadoop, Sqoop, Mahout, and Pig, leads to a broad set of tools for extracting, processing, and learning from existing and new data." @default.
- W1666440859 created "2016-06-24" @default.
- W1666440859 creator A5063827272 @default.
- W1666440859 date "2014-11-03" @default.
- W1666440859 modified "2023-10-17" @default.
- W1666440859 title "Machine Learning as a Batch Process" @default.
- W1666440859 doi "https://doi.org/10.1002/9781119183464.ch10" @default.
- W1666440859 hasPublicationYear "2014" @default.
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