Data cleaningwith different tools for big data analytics

Abstract

Data forms the backbone of any data analytics. Regarding data, there are many things to go wrong be it the construction, arrangement, formatting, spellings, duplication, extra spaces, and so on. To perform the data analytics properly it is necessary to use various data cleaning techniques so that the data is ready for analysis.Thus, it is important to grow accustomed to the process of data cleaning techniques and all of the data cleansing tools that are related to data cleansing methods. Data cleansing or data cleaning is the process of identifying and removing inaccurate records from a dataset, table, or database and refers to recognising unfinished, unreliable, inaccurate or non-relevant parts of the data and then restoring, remodelling, or removing the dirty or crude data.Data cleaning techniques may be performed as batch processing through scripting or interactively with data cleansing tools.After cleaning, a dataset should be uniform with other related datasets in the operation. The discrepancies identified or eliminated may have been basically caused by user entry mistakes, by corruption in storage or transmission, or by various data dictionary descriptions of similar items in various stores.With the rise of big data, data cleaning methods has become more important than ever before. Every industry, banking, healthcare, retail, hospitality, education is now navigating in a large ocean of data.