SQL and Big Data Gaining Greater Insights

¶ … 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 of big data applications that include SQL command structures the faster these applications will mature and become engrained in the most important business processes of an organization (Wagner, Pant, 2010). When that happens, big data's contributions will in turn accelerate and its value be even more visible throughout enterprises. One of the most valuable insights gained from completing the analysis for this paper is how critical it is for big data applications to a widespread acceptance in enterprises to prove their value, while also illustrating the innate value of analytics in better understanding customer, market and production dynamics of a given business (Kim, Lund, Dombrowski, 2013). Suitability of big data analytics in general and those specifically delivered with SQL-centric user interfaces are increasing enterprise adoption rates of these technologies, in turn making them more valuable (Kiron, 2012) .From a long-term perspective, usability of big data and its associated elements including Hadoop clusters, MapReduce Application Programmer Interfaces (APIs) and underlying Hadoop and R. data structures all require exponentially larger storage systems and technologies. This requirement is also a powerful catalyst for the development of integrated data sets that can be managed at the operating system level through more effective file structures and file systems. Banks and financial institutions have been continually investing in big data and the enhancement of standardized file system architectures to streamline advanced analysis of transactions, customers and their internal process areas (Meijer, Bierman, 2011). In short, the suitability of SQL as a critical component of big data strategies continues to be proven daily by the workloads that banks, financial institutions and many enterprises plaice on them. As the underlying big data architectures continue to mature, SQL will progress from being used just for querying and report-writing to actual data management. Today this area is just beginning to evolve, and will be discussed in the next section of this analysis that analyzes the challenges and opportunities of using relational databases for traditional enterprises workloads.

Of the many challenges inherent in using SQL as a query technology first and later as a technology to organize big data, the greatest challenge is in the variation in the data structures themselves. The SQL database schema or structure is specifically designed to ensure a highly structured data model that is also fully relational (Meijer, Bierman, 2011). Big data encompasses both unstructured and structured data and therefore requires data structures that can compensate for the wide differences in data characteristics. The challenges of incouding to widely divergent data sets, even within a given series of Hadoop clusters, is that of consistency of the data models themselves and their cross-integration (Ferguson, 2012). These challenges of data integration will continue to be an issue for enterprises attempting to unify their divergent and often disconnected systems of record throughout their organizations (Kubick, 2012). Even with scalable SQL data structures, there still is a very significant challenge of making all data included in a Hadoop or R. distribution consistent with one another down to the record level. Further complicating this issue is the need for including structured data modeling and if necessary, structured taxonomies to enable SQL-based data modeling, below the level of the basic graphic interface. SQL today is most often used at this user interface level, which drives up adoption of big data-based applications yet has also over time restricted SQL use in the underlying data model, even at the object level of compensate for unstructured data (Rys, 2011).