Big Data Tools—Hadoop Ecosystem, Spark and NoSQL Databases

2019 ◽  
pp. 83-165
Author(s):  
C.S.R. Prabhu ◽  
Aneesh Sreevallabh Chivukula ◽  
Aditya Mogadala ◽  
Rohit Ghosh ◽  
L.M. Jenila Livingston
Keyword(s):  
Big Data ◽  
Nosql Databases ◽  
Hadoop Ecosystem

scholarly journals Benchmarking Big Data OLAP NoSQL Databases

2018 ◽  
pp. 82-94 ◽  
Author(s):  
Mohammed El Malki ◽  
Arlind Kopliku ◽  
Essaid Sabir ◽  
Olivier Teste
Keyword(s):  
Big Data ◽  
Nosql Databases

Formalizing the Mapping of UML Conceptual Schemas to Column-Oriented Databases

2018 ◽  
Vol 14 (3) ◽  
pp. 44-68 ◽  
Author(s):  
Fatma Abdelhedi ◽  
Amal Ait Brahim ◽  
Gilles Zurfluh
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Relational Databases ◽  
Traditional Approach ◽  
Physical Models ◽  
Decision Making Process ◽  
Nosql Databases ◽  
Care Field ◽  
Sufficient Degree

Nowadays, most organizations need to improve their decision-making process using Big Data. To achieve this, they have to store Big Data, perform an analysis, and transform the results into useful and valuable information. To perform this, it's necessary to deal with new challenges in designing and creating data warehouse. Traditionally, creating a data warehouse followed well-governed process based on relational databases. The influence of Big Data challenged this traditional approach primarily due to the changing nature of data. As a result, using NoSQL databases has become a necessity to handle Big Data challenges. In this article, the authors show how to create a data warehouse on NoSQL systems. They propose the Object2NoSQL process that generates column-oriented physical models starting from a UML conceptual model. To ensure efficient automatic transformation, they propose a logical model that exhibits a sufficient degree of independence so as to enable its mapping to one or more column-oriented platforms. The authors provide experiments of their approach using a case study in the health care field.

NoSQL Databases

2014 ◽  
pp. 186-215 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Data Storage ◽  
Big Data Processing ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

Big Data Analytics

2019 ◽  
pp. 67-81
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Big Data ◽  
Open Problem ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Capture ◽  
Data Sets ◽  
Full Potential ◽  
Processing Application ◽  
Nosql Databases ◽  
The World

The world is increasingly driven by huge amounts of data. Big data refers to data sets that are so large or complex that traditional data processing application software are inadequate to deal with them. Healthcare analytics is a prominent area of big data analytics. It has led to significant reduction in morbidity and mortality associated with a disease. In order to harness full potential of big data, various tools like Apache Sentry, BigQuery, NoSQL databases, Hadoop, JethroData, etc. are available for its processing. However, with such enormous amounts of information comes the complexity of data management, other big data challenges occur during data capture, storage, analysis, search, transfer, information privacy, visualization, querying, and update. The chapter focuses on understanding the meaning and concept of big data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.

Secure search for encrypted personal health records from big data NoSQL databases in cloud

Computing ◽  
2019 ◽  
Vol 102 (6) ◽  
pp. 1521-1545 ◽  
Author(s):  
Lanxiang Chen ◽  
Nan Zhang ◽  
Hung-Min Sun ◽  
Chin-Chen Chang ◽  
Shui Yu ◽  
...  
Keyword(s):  
Big Data ◽  
Personal Health Records ◽  
Personal Health ◽  
Health Records ◽  
Nosql Databases ◽  
Secure Search

scholarly journals When Relational-Based Applications Go to NoSQL Databases: A Survey

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 241
Author(s):  
Geomar A. Schreiner ◽  
Denio Duarte ◽  
Ronaldo dos S. Melo
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Relational Databases ◽  
Research Area ◽  
Relational Data ◽  
Data Sets ◽  
System Architectures ◽  
Nosql Databases ◽  
State Of Art ◽  
Open Issues

Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution.

scholarly journals A survey of open source tools for machine learning with big data in the Hadoop ecosystem

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Sara Landset ◽  
Taghi M. Khoshgoftaar ◽  
Aaron N. Richter ◽  
Tawfiq Hasanin
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Hadoop Ecosystem
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