Comparative Analysis of ETL Tools in Big Data Analytics
Abstract
Massive amounts of data are being generated these days all around the world. The majority of social networking platforms, ecommerce sites, the public and private sectors, healthcare institutions, cloud networks, and various servers are all generating immense amount of data. Collected data from various sites could be in a structured or unstructured format. Extract Transform Load (ETL) is a crucial component of the growing demand for quicker business decisions aimed at many contemporary applications. Due to the volume and speed of data, real-time ETL is built on the foundation of multi-source, unstructured data stream extraction and transformation employing disc data in dispersed environments. The entire procedure is pipelined while processing so that the final resultant data can provide some essential and useful conclusions to work on. Some analytical findings are once again helpful in making decisions. However, the produced results may differ in some numbers, graphs, and figures in many circumstances. This occurs as a result of the usage of some unrealistic tools for big data processing. This paper proposed several methods and appropriate ETL (extract, transform, and load) tools for the big data processing, which may result in appropriate analytics and conclusions from the data.
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