Efficient data management tools for the heterogeneous big data warehouse

The traditional RDBMS has been consistent for the normalized data structures. RDBMS served well for decades, but the technology is not optimal for data processing and analysis in data intensive fields like social networks, oil-gas industry, experiments at the Large Hadron Collider, etc. Several challenges have been raised recently on the scalability of data warehouse like workload against the transactional schema, in particular for the analysis of archived data or the aggregation of data for summary and accounting purposes. The paper evaluates new database technologies like HBase, Cassandra, and MongoDB commonly referred as NoSQL databases for handling messy, varied and large amount of data. The evaluation depends upon the performance, throughput and scalability of the above technologies for several scientific and industrial use-cases. This paper outlines the technologies and architectures needed for processing Big Data, as well as the description of the back-end application that implements data migration from RDBMS to NoSQL data warehouse, NoSQL database organization and how it could be useful for further data analytics.