Comparative Study of Big Data Computing and Storage Tools: A Review

Prasad, Bakshi Rohit; Agarwal, Sonali

doi:10.14257/ijdta.2016.9.1.05

Cited by 41 publications

(14 citation statements)

References 30 publications

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“…It is a research of "the effort to resolve conflicts within hardware or software based on the process of re-engineering a system or a network". According to this requirement, big data is considered as a massive information collector of different data (structured, semi-structured, and unstructured data [17]), which can be interfaced with the ESB in order to facilitate data migration between databases having different technologies and data transfer into a communication system. If compared with traditional relational database systems, big data is mostly indicated for the resolution of hardware inefficiencies linked to real time data processing [18].…”

Section: Introductionmentioning

confidence: 99%

A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration

Massaro¹,

Calicchio²,

Maritati³

et al. 2018

IJAIA

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In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).

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Section: Introductionmentioning

confidence: 99%

A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration

Massaro¹,

Calicchio²,

Maritati³

et al. 2018

IJAIA

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“…A recent study reviews the trends of storage and computing tools with their relative capabilit ies, limitations and environment they are suitable to work with [23]. While h igh-end platforms like IBM Netezza AMPP could cater to Big Data, due to economic considerations, choices such as Hadoop have proliferated world-wide resulting in the rise of NoSQL database adoption that can integrate easily with Hadoop.…”

Section: Resultsmentioning

confidence: 99%

SQL Versus NoSQL Movement with Big Data Analytics

Venkatraman¹,

Fahd²,

Kaspi³

et al. 2016

IJITCS

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Abstract-Two main revolutions in data management have occurred recently, namely Big Data analytics and NoSQL databases. Even though they have evolved with different purposes, their independent developments complement each other and their convergence would benefit businesses tremendously in making real-t ime decisions using volumes of co mplex data sets that could be both structured and unstructured. While on one hand many software solutions have emerged in supporting Big Data analytics, on the other, many NoSQL database packages have arrived in the market. However, they lack an independent benchmarking and co mparat ive evaluation. The aim of this paper is to provide an understanding of their contexts and an in-depth study to compare the features of four main NoSQL data models that have evolved. The performance comparison of traditional SQL with No SQL databases for Big Data analytics shows that NoSQL database poses to be a better option for business situations that require simplicity, adaptability, high performance analytics and distributed scalability of large data. This paper concludes that the NoSQL movement should be leveraged for Big Data analytics and would coexist with relational (SQL) databases.

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“…The authors also present a performance comparison between Spark, Shark, and Impala using queries containing scans, aggregations, and joins, concluding that Spark SQL is substantially faster than Shark and generally competitive with Impala. Prasad and Agarwal's () recent trends of storage and computing tools are analyzed, showing their relative capabilities, limitations, and most suitable environment. This work also presents a detailed description of four Big Data storage tools (HBase, Hive, Neo4j, and Cassandra) and four computing tools (Hadoop, Impala, IBM Netezza, and Apache Giraph).…”

Section: Related Workmentioning

confidence: 99%

Big data processing tools: An experimental performance evaluation

Rodrigues

Santos

Bernardino

2018

WIREs Data Min & Knowl

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Big Data is currently a hot topic of research and development across several business areas mainly due to recent innovations in information and communication technologies. One of the main challenges of Big Data relates to how one should efficiently handle massive volumes of complex data. Due to the notorious complexity of the data that can be collected from multiple sources, usually motivated by increasing data volumes gathered at high velocity, efficient processing mechanisms are needed for data analysis purposes. Motivated by the rapid growth in technology, development of tools, and frameworks for Big Data, there is much discussion about Big Data querying tools and, specifically, those that are more appropriated for specific analytical needs. This paper describes and evaluates the following popular Big Data processing tools: Drill, HAWQ, Hive, Impala, Presto, and Spark. An experimental evaluation using the Transaction Processing Council (TPC‐H) benchmark is presented and discussed, highlighting the performance of each tool, according to different workloads and query types. This article is categorized under: Technologies > Computer Architectures for Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Data Preprocessing Application Areas > Data Mining Software Tools

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Comparative Study of Big Data Computing and Storage Tools: A Review

Abstract: As a result of tremendous rise in internet usage

Cited by 41 publications

References 30 publications

A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration

A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration

SQL Versus NoSQL Movement with Big Data Analytics

Big data processing tools: An experimental performance evaluation

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