An Essay of Data warehouses with Big Data Example | Topics and Well Written Essays - 1250 words

An of Data warehouses with Big Data - Essay Example Basically, data warehousing is a relatively more intelligent and refined database administration system which can handle large amount of data. At the commercial level, data warehousing is further evolving in the form of Big Data which is aimed at storing and retrieving enormous amount of asynchronous and disparate data across distributed computing systems (Kusnetzky 2010). According to Ricardo (2011), traditional form of database administration is based on the fundamental technique of identifying and manipulating the different characteristic entities in a given dataset. These entities can be termed as database elements. Most important database elements are forms, fields, tables, and queries. In an RDBMS framework, these elements of a database are related with each other using simple administrator defined relationships. RDBMS can thus help an administrator to organize data in an intelligent and retrievable way. However, this technology is not always helpful to arrange data into multip le layers so as to facilitate more efficient stacking, less errors, and context aware distribution. Although the basic concepts of RDBMS are still in extensive use in different high level database applications (Kimball and Ross 2011), large scale information storage services are now progressing toward multidimensional management of data. Evolution of DW schema took place with regard to the needs of the industries and research institutes. It can be stated that evolution of data warehousing was initially aimed to mitigate the limitations of preexisting database management systems. According to Baru et al (2013), the database management industry has considerably matured and developed its own dynamics and techniques over the last twenty years. But in the past few years, data warehousing technologies have become commercially important. With the advent of Big Data, the database management industry has now developed â€Å"increased volume, velocity, and variety† of data storage, ret rieval, and even processing systems (Baru et al 2013, p. 60). Experts like Devlin (2011) have gone to the extent of stating that Big Data is a better and independent form of database administration technology vis-a-vis data warehousing. But the author appears to be more critical toward the traditional data warehousing technologies. From a holistic viewpoint, synchronization of data warehousing with cloud computing facilities is precondition to Big Data (Baru et al 2013; Kusnetzky 2010). Consequently, â€Å"taxonomy of data† (see Devlin 2011, section 2) in the realm of Big Data can be regarded as a viable cornerstone in the evolution of contemporary DW schema. For more details, refer to Figure – 1. Figure – 1: Taxonomy of data as viewed at the point of transition from data warehousing to Big Data techniques. The figure shows six main varieties of data named multiplex, textual, compound, derived, atomic, and measurement. (Devlin 2011, section 2) RDBMS is a databas e management system that is based on defining, linking, and organizing different database elements like tables, forms, queries, etc. However, a standard DW schema gives maximum importance to the data tables. As such, data tables are organized with the help of â€Å"dimensional modeling† (Kimball and Ross 2011, p. 16). This is a method of database administration which is based on simplicity and architectural coherence of distributed database systems with complex warehousing