Data Warehousing and Analytics: An Examination of Performance through Cloud and RDBMS Technologies

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Name Instructor Course Date Data Warehousing and Analytics: An Examination of Performance through Cloud and RDBMS Technologies Preliminary Experiments Growths in technology have led to the production of large data volumes. In effect, the amount of data produced across several domains has significantly increased. An increase in the amount of data being processed has made it difficult for analysts to query large data repositories. The use of an ideal database system plays a vital role in enabling data handlers to identify the best large scale data analysis system that meets their needs and expectations. The results garnered from the experimental analyses play influential roles in informing users of why it is recommended to consider the use of a particularly large-scale data access system over another. Particularly, the analysis on the use of DBMS-X, HadoopDB, and Hive databases play key roles in developing specific reports regarding the reliability of using particular systems to support large-scale declarative queries. The choice of a database is driven by its performance and its ability to identify the time required to get all the answers to a query. Understanding the primary features of Hive, DBMS-X and those of Hadoop plays key roles in enabling experts to select an ideal database to use. Hadoop databases provide a framework upon which large data sets across several computers can be processed. Hadoop is touted to offer efficient performance enhancements that give a platform upon which high-throughput access to data and streaming can take place. Moreover, of the distributed database options, Hadoop has proved to be relatively inexpensive yet offers a higher

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