In a world rich in interconnected and complex data, the non-relational database paradigm can better handle large volumes of data at high speed with a scale-out architecture, which are two essential requirements for large industries and world-class applications. This article presents AMANDA, a flexible middleware for automatic migration between relational and non-relational databases based on a user-defined schema that offers support for multiple sources and target databases. We evaluate the performance of AMANDA by assessing the migration speed, query execution, query performance, and migration correctness, from two Relational Database Management Systems (RBMSs), i.e., Postgres and MySQL, to a non-relational database (NoSQL), i.e., DGpraph. The results show that AMANDA successfully migrates data 26 times faster than previous approaches, when considering Northwind. Regarding the IMDB database, it took 7 days to migrate 5.5 GB of data.
In past decades, the requirements that database management systems (DBMSs) must achieve have become increasingly stringent (speed, data volume). This increase in complexity led to the development of a wide range of non-relational databases strategies, each one suited for specific scenarios. In this context, Graph Database Management Systems (GDBMSs) became popular to represent social networks and other domains that can be intuitively represented as graph-like structures. In this paper, we represent Version Control System data, specifically Git, from a large software project in a graph structure and compared three popular GDBMSs: Neo4j, Janus Graph and Dgraph. We evaluated read/write operations performance for common activities, such as inserting new commits into the graph and retrieving the complete commit history of a specific project. With this contribution, researches and engineers may choose, assertively, the better solution for their needs.
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