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Big Data Warehouse: Building Columnar NoSQL OLAP Cubes

Big Data Warehouse: Building Columnar NoSQL OLAP Cubes
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Author(s): Khaled Dehdouh (Computer Engineering Department of Cherchell Military Academy, TIPAZA Algeria), Omar Boussaid (ERIC Laboratory/ University of Lyon 2, Bron, France) and Fadila Bentayeb (ERIC Laboratory/ University of Lyon, Lyon 2, Bron, France)
Copyright: 2020
Volume: 12
Issue: 1
Pages: 24
Source title: International Journal of Decision Support System Technology (IJDSST)
Editor(s)-in-Chief: Pascale Zaraté (Toulouse University, France)
DOI: 10.4018/IJDSST.2020010101

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Abstract

In the Big Data warehouse context, a column-oriented NoSQL database system is considered as the storage model which is highly adapted to data warehouses and online analysis. Indeed, the use of NoSQL models allows data scalability easily and the columnar store is suitable for storing and managing massive data, especially for decisional queries. However, the column-oriented NoSQL DBMS do not offer online analysis operators (OLAP). To build OLAP cubes corresponding to the analysis contexts, the most common way is to integrate other software such as HIVE or Kylin which has a CUBE operator to build data cubes. By using that, the cube is built according to the row-oriented approach and does not allow to fully obtain the benefits of a column-oriented approach. In this article, the focus is to define a cube operator called MC-CUBE (MapReduce Columnar CUBE), which allows building columnar NoSQL cubes according to the columnar approach by taking into account the non-relational and distributed aspects when data warehouses are stored.

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