Not Only SQL Databases Research Articles

NoSQL Database Technologies

ABSTRACT As cloud computing continues to evolve, organizations are finding new ways to store the massive amounts of big data that are collected. Big data storage often require greater flexibility and scalability which can be provided by incorporating NoSQL technologies. NoSQL (Not Only SQL) is quickly becoming a popular approach to store large and unstructured data. This paper looks at the various classifications of NoSQL technologies as well as many of the notable characteristics of the technologies. The authors also describe some deficiencies of using NoSQL and give some explanation to why companies are adopting the technology. The paper concludes with suggestions for future research of NoSQL technologies and a content analysis of current articles in database management is provided in the appendix. Keywords: big data, cloud computing, document databases, MongoDB, NoSQL, Not Only SQL, unstructured database INTRODUCTION Since NoSQL database technologies entered the market in 1998, these technologies have challenged the widely-accepted relational structures and database management practices supported by traditional SQL databases. Despite this trend, the perception that NoSQL seeks to eliminate the use of relational databases is a common misconception. NoSQL is best considered with the acronym NOSQL - Not Only SQL - which more accurately represents an approach that combines non-relational databases with the use of relational ones. This approach seeks to leverage both NoSQL and SQL technologies in order to balance the demands of performance, scalability, and schema flexibility with data integrity and consistency (Purdue, 2014; Penchikala, 2013). The rise of web-scale applications creates performance, scalability, and schema flexibility demands that present significant challenges for traditional SQL / relational databases. Thus, a variety of new entrants have entered the market, each seeking to address specific challenges, including (Kent, 2012): * The need to store large volumes of data about users, objects and products * The need to provide near-real-time transaction processing * The need to provide high-speed analytics on large volumes of data (big data) * The need to support flexible data schemas for evolving business environments * The need to scale applications horizontally with predictable hardware and licensing cost structures Each NoSQL solution seeks to address one or more of the above issues, often by providing alternative approaches to long-established relational database norms. Traditional relational databases were not designed to cope with the scalability and flexibility challenges facing modern applications. Older databases were not designed to take advantage of the inexpensive storage and excess processing power available today. The defining characteristics of NoSQL technologies, as well as the relative advantages and disadvantages of NoSQL, and the subsequent influence on adoption of NoSQL technologies are each the subject of a small, yet growing body of research. This paper provides a literature review of that research and proposes potential areas where future research may focus. REVIEW OF LITERATURE NoSQL database technology is a relatively recent innovation and much of the research surrounding it is of an emerging nature. For the purpose of this literature review Galileo was primarily used for identifying the articles. Articles were selected that focused directly on NoSQL database technologies (unstructured / semi-structured databases, document databases, etc.) and articles that focused on relational databases. The contrasting articles provided the researchers with the ability to compare the features that are offered with a greater degree of variability in NoSQL databases (for example, an article on SQL scalability provided a foundation for comparing newer horizontal scaling techniques used in certain NoSQL databases). …

Applying data models to big data architectures

A key message from the early adopters of big data is that technologies such as Hadoop®, NoSQL (Not Only Structured Query Language) databases, and stream computing should not be seen as completely separate technologies but are more valuable when deployed in conjunction with more traditional data management components. There is an urgent need for an overall blueprint for treating both the new and traditional data management components in a holistic and integrated manner. A models-driven approach ensures consistency across this data management landscape in terms of management, governance, and efficiency. This paper focuses on the data modeling considerations relating the big data deployment using the examples of transaction data and mixed unstructured data to ensure that data components are evolved to maximize business value and development efficiencies.

A Comparative Study of NoSQL Databases

In today’s scenario, web applications are facing new challenges while serving millions of users. It can be easily understood and appreciated that while accessing these web applications from all over the world, users expect the services to be always available with high performance and reliability. The rate of growth of successful web services is much faster as compared to increase in the performance of computer hardware. Therefore, after sometime a web application which attempts to provide large number of web services and support large number of users needs the ability to scale. This study explores information on various NoSQL (Not only Structured Query Language) databases and attempts to make a comparison between them based on different criteria. The NoSQL databases were created to offer high performance and high availability especially for large scale and high concurrency applications at the expense of losing the ACID (Atomic, Consistent, Isolated, Durable) properties of the traditional databases in exchange with a weaker BASE (Basic Availability, Soft state, Eventual consistency) features. Keywords: Big Data;NoSQL; Scalability; Sharding; Replication; Comparison