¶ … SQL and Big Data
Gaining greater insights into terabytes of unstructured and structured data organizations have been collecting in many cases for decades across diverse computing and storage platforms are increasingly being unified through advanced data and system architectures. Big Data is the term used to define very large, diverse data sets that contain both structured and unstructured data that defy analysis using conventional database management and analytics applications (International Journal of Micrographics & Optical Technology, 2010). Big Data is an area generating much interest in enterprises as this collection of data analysis, aggregation and extraction techniques continue to deliver valuable insights into how companies can become more competitive (Datskovsky, 2013). Structured Query Language (SQL) is a widely accepted approach to querying databases, aggregating and analyzing data and creating useful reports that guide decision making in organizations (Rys, 2011). Enterprise software companies are creating Big Data analytics applications that include SQL-like interfaces so business analysts, programmers and line-of-business executives can gain access to the vast data sets they had previous not been able to use for guiding their strategic and tactical decision-making (Meijer, Bierman, 2011).
Challenges, Opportunities and Suitability of Using Relational Databases and SQL for Processing Big Data
The current direction of new application development for big data applications is increasingly reliant on SQL-based interfaces, commands, reporting and query approaches to streamline access and use of this emergent class of applications (Baker, 2013). Relying on SQL-based interfaces and SQL-like commands, big data application providers are attempting to increase the adoption and use of their software across enterprises of all sizes and industries (Ferguson, 2012). The greater the adoption...
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