Abstract: The world as we know is much more competitive as it was a decade back. Therefore, the restive companies are trying to lead their competitors and for that they need to have certain mechanism from which they can serve the best in their respective domains. The term Big Data can be inferred as the large amount of data stored among the databases and servers of these big Multinational companies (MNCs), which is basically used to identify patterns which can influence future predictions. As per the Oracle survey, 90% of the world's data has been coagulated in the last two years. MNCs are trying to hold this data into their Data warehouses and thus making it available for data mining and analytics. In such scenarios, there is an insisting need of some powerful tools and programs which can handle such huge data. One such tool is provided by SAS (Statistical Analysis System) company to handle the migration of the Data into Data warehouses from different sources. This process is commonly known as extraction, transformation and loading (ETL). But sometimes these tools are further optimize to increase their efficiency in terms of time and efforts. One such program has been shared in this paper as a motive to decrease the loading cycle of Big Data and save time, efforts and money.
Keywords: SAS; Data warehouses; Big Data, ETL.
Title: % BigDataLoader: A SAS Macro to Migrate Big Data 99% Faster
Author: Anant Sharma
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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