11/15/2023 0 Comments Data crunch meaningHowever, appropriate infrastructure is necessary to have the computing power for such operations. Therefore, particularly with large data sets and relational databases, data crunching can be a significant advantage. Extraction of raw data in order to prepare for subsequent evaluation.Īs a rule, a lot of time can be saved with data crunching because the processes do not need to be performed manually.The correction of errors in data sets, whether spelling errors or program errors.The conversion of one format to another, for example, plain text to XML data records.Further processing of inherited data within a program code.This trichotomy has the advantage that the individual data (input, output) can also be used for other scenarios. Finally, the data is output in the correct format, so it can be further processed or analyzed. First, the raw data is read in order to convert it into a selected format as the next step. Most data crunching tasks can be simplified into three steps. This means that the program sequences may be repeated until the desired result is achieved: an accurate, correct data set that can be further processed directly or imported and does not contain any errors or bugs. This process, as the data analysis itself, can be iterative when the output of the crunching process includes new data or errors. Data crunching is therefore an upstream process of data analysis. Data crunching is more about correct processing, so that a system can do something with the records and the data format. Depending on the context, different programming languages and tools are used: While Excel, Batch and Shell programming were used earlier languages like Java, Python or Ruby are preferred today.ĭata crunching, however, does not refer to exploratory analysis or the visualization of data – that is done by special programs which are tailored to their area of application. Other areas where data crunching applies are medicine, physics, chemistry, biology, finance, criminology, or web analytics. The ultimate goal of data processing is deeper insight into the matter that should be conveyed with the data, such as in the field of business intelligence, so that informed decisions can be made.
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