{"id":3230,"date":"2023-05-24T08:14:43","date_gmt":"2023-05-24T06:14:43","guid":{"rendered":"https:\/\/www.risc-software.at\/fachbeitrag-methoden-und-werkzeuge-fuer-die-datenaufbereitung-im-big-data-bereich\/"},"modified":"2024-11-06T17:35:28","modified_gmt":"2024-11-06T16:35:28","slug":"technical-article-methods-and-tools-for-data-preparation-in-the-big-data-area","status":"publish","type":"publication","link":"https:\/\/www.risc-software.at\/en\/technicalarticles\/technical-article-methods-and-tools-for-data-preparation-in-the-big-data-area\/","title":{"rendered":"Methods and tools for data preparation in the big data area"},"content":{"rendered":"\n

by DI Paul Heinzlreiter<\/h3>\n\n\n
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In recent years, the role of big data in numerous economic sectors such as the manufacturing industry, logistics or trade has become increasingly important. Using a wide variety of sensor systems, large amounts of data are collected that can subsequently be used to optimize machines or business processes. Methods from the fields of artificial intelligence, machine learning or statistics are often used here.<\/p>\n\n\n\n

However, all these methods require a larger quantity of high-quality and valid data<\/a> as a basis. In this context, data engineering<\/a> is used to collect the raw data, cleanse it and merge it into an integrated database. While a previous article<\/a> (the magazine INSIGHT #1) highlighted the general role and goals of data engineering, this article will focus on methods and proven tools as well as provide an exemplary insight into the algorithmic implementation of data engineering tasks.<\/p>\n\n\n\n



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Table of contents<\/strong><\/p>\n\n\n\n