Currently, data and its exploitation play a very important role in the life of individuals and organizations, without forgetting that through data management tools these organizations store their data history, which guides them at the operational and strategic levels. Considering the explosion of data at huge sizes, the old relational system has several shortcomings in managing this information, which justifies the need for data migration to NoSQL systems, which manage BigData efficiently. In this sense, this article fits to find a new transformation of relational databases to MongoDB, as a wellchosen NoSQL system for this transformation. This article presents the problem of this transformation, so that it can be discussed based on related works, then enlightened by presenting the storage philosophy and the semantics of the two systems source and destination of this transformation, which will be chained by our approach which defining a set of transformation rules to keep the same data and advantages of relational systems under a structure adequate to the concept of MongoDB and finally our conclusion of this work.
Today, in the computer science world, data has become an essential hub for information processing in general. These data continue to progress in a progressive and exponential way, especially in storage and its technologies. The term big data as we use it for mass volumes of data, offers important techniques for processing, analyzing, and proposing useful information for decision making. NOSQL databases become a modern and indispensable technology to use, to provide scalability to support large data. This is why there is always this challenge for organizations to transform their existing databases to NOSQL databases by considering the heterogeneity and complexity of relational data. In this paper, we propose an approach for the migration of a relational database to another NOSQL. This method has two phases, the first is to transform the relational database to a NOSQL database, and the second is to enhance and improve the quality of the data with cleanup processes to provide and prepare them for big data analytics systems.
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