With the advent of the Internet of Things (IoT) and cloud computing, the need for data stores that would be able to store and process big data in an efficient and cost-effective manner has increased dramatically. Traditional data stores seem to have numerous limitations in addressing such requirements. NoSQL data stores have been designed and implemented to address the shortcomings of relational databases by compromising on ACID and transactional properties to achieve high scalability and availability. These systems are designed to scale to thousands or millions of users performing updates, as well as reads, in contrast to traditional RDBMSs and data warehouses. Although there is a plethora of potential NoSQL implementations, there is no one-sizefit-all solution to satisfy even main requirements. In this paper, we explore popular and commonly used NoSQL technologies and elaborate on their documentation, existing literature and performance evaluation. More specifically, we will describe the background, characteristics, classification, data model and evaluation of NoSQL solutions that aim to provide the capabilities for big data analytics. This work is intended to help users, individuals or organizations, to obtain a clear view of the strengths and weaknesses of well-known NoSQL data stores and select the right technology for their applications and use cases. To do so, we first present a systematic approach to narrow down the proper NoSQL candidates and then adopt an experimental methodology that can be repeated by anyone to find the best among short listed candidates considering their specific requirements.
Citation: Hamzeh Khazaei, Marios Fokaefs, Saeed Zareian, Nasim Beigi-Mohammadi, Brian Ramprasad, Mark Shtern, Purwa Gaikwad, Marin Litoiu. How do I choose the right NoSQL solution? A comprehensive theoretical and experimental survey[J]. Big Data and Information Analytics, 2016, 1(2&3): 185-216. doi: 10.3934/bdia.2016004
With the advent of the Internet of Things (IoT) and cloud computing, the need for data stores that would be able to store and process big data in an efficient and cost-effective manner has increased dramatically. Traditional data stores seem to have numerous limitations in addressing such requirements. NoSQL data stores have been designed and implemented to address the shortcomings of relational databases by compromising on ACID and transactional properties to achieve high scalability and availability. These systems are designed to scale to thousands or millions of users performing updates, as well as reads, in contrast to traditional RDBMSs and data warehouses. Although there is a plethora of potential NoSQL implementations, there is no one-sizefit-all solution to satisfy even main requirements. In this paper, we explore popular and commonly used NoSQL technologies and elaborate on their documentation, existing literature and performance evaluation. More specifically, we will describe the background, characteristics, classification, data model and evaluation of NoSQL solutions that aim to provide the capabilities for big data analytics. This work is intended to help users, individuals or organizations, to obtain a clear view of the strengths and weaknesses of well-known NoSQL data stores and select the right technology for their applications and use cases. To do so, we first present a systematic approach to narrow down the proper NoSQL candidates and then adopt an experimental methodology that can be repeated by anyone to find the best among short listed candidates considering their specific requirements.
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Hamzeh Khazaei, Marios Fokaefs, Saeed Zareian, Nasim Beigi-Mohammadi, Brian Ramprasad, Mark Shtern, Purwa Gaikwad, Marin Litoiu. How do I choose the right NoSQL solution? A comprehensive theoretical and experimental survey[J]. Big Data and Information Analytics, 2016, 1(2&3): 185-216. doi: 10.3934/bdia.2016004
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