A Scalable Business Intelligence Decision-Making System in the Era of Big Data

Abstract

Transformation presents the second step in the ETL process that is responsible for extracting, transforming and loading data into a data warehouse. The role of transformation is to set up several operations to clean, to format and to unify types and data coming from multiple and different data sources. The goal is to get data to conform to the schema of the data warehouse to avoid any ambiguity problems during the data storage and analytical operations. Transforming data coming from structured, semi-structured and unstructured data sources need two levels of treatments: the first one is transformation schema to schema to get a unified schema for all selected data sources and the second treatment is transformation data to data to unify all types and data gathered. To ensure the setting up of these steps we propose in this paper a process switch from one database schema to another as a part of transformation schema to schema, and a meta-model based on MDA approach to describe the main operations of transformation data to data. The results of our transformations propose a data loading in one of the four schemas of NoSQL to best meet the constraints and requirements of Big Data.