Big-Parallel-ETL: New ETL for Multidimensional NoSQL Graph Oriented Data

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Abstract

The quantitative explosion of digital data derived from social networks, smart devices, IoT sensors, etc is eventuated by the Big Data concept considered as a very important aspect in the performance improvement of traditional decision-making systems since it reveals serious challenges to be addressed. Therefore, the main purpose of this research paper is the integration of NoSQL Graph-oriented Data into Data Warehouse to deal with Big Data challenges especially with the absence of similar approaches to the best of our knowledge. In this paper, we propose a new approach called Big-Parallel-ETL that aims to adapt the classical ETL process (Extract-Transform-Load) with Big Data technologies to accelerate data handling based on the famous MapReduce concept characterized by its efficient parallel processing feature. Our solution proposes a set of detailed Algorithms based on several rules able to conceive rapidly and efficiently the target multidimensional structure (dimensions and facts) from the NoSQL Graph oriented database.

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APA

Soussi, N. (2021). Big-Parallel-ETL: New ETL for Multidimensional NoSQL Graph Oriented Data. In Journal of Physics: Conference Series (Vol. 1743). IOP Publishing Ltd. https://doi.org/10.1088/1742-6596/1743/1/012037

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