2 results
Search Results
Now showing 1 - 2 of 2
Item Open Access TEACHING BIG DATA ANALYTICAL PLATFORMS IN HIGHER EDUCATION FOR GRADUATE DEGREE STUDENTS(СДУ хабаршысы - 2018, 2018) Nurkey U.T. ; Bogdanchikov A.Abstract. An extensive archive of petabytes of data has been generated from modern information systems and digital technologies such as scientific data analysis, social data analysis, reference systems and Internet services journals. To investigate and extract knowledge from this enormous data much effort is needed. Due to this, Big Data Management Systems need to be integrated as part of the computing curriculum. In this article, we present examples of analysed tasks that can be processed as large data projects using Apache Hadoop, and it’s Map – Reduce, Apache Spark, Hive and Pig by demonstrating how each type of system can be integrated via sample datasets and data analysis tasks. The aim of this paper is to show how Big data analyzing tools can be educated through sample tasks, their solution and implementation.Item Open Access MapReduce Solutions Classification by Their Implementation(The International Journal of Engineering Pedagogy (iJEP), 2023) Orynbekova K.; Bogdanchikov A.; Cankurt S.; Adamov A.; Kadyrov Sh.Distributed Systems are widely used in industrial projects and scientific research. The Apache Hadoop environment, which works on the MapReduce paradigm, lost popularity because new, modern tools were developed. For example, Apache Spark is preferred in some cases since it uses RAM resources to hold intermediate calculations; therefore, it works faster and is easier to use. In order to take full advantage of it, users must think about the MapReduce concept. In this paper, a usual solution and MapReduce solution of ten problems were compared by their pseudocodes and categorized into five groups. According to these groups’ descriptions and pseudocodes, readers can get a concept of MapReduce without taking specific courses. This paper proposes a five-category classification methodology to help distributed-system users learn the MapReduce paradigm fast. The proposed methodology is illustrated with ten tasks. Furthermore, statistical analysis is carried out to test if the proposed classification methodology affects learner performance. The results of this study indicate that the proposed model outperforms the traditional approach with statistical significance, as evidenced by a p-value of less than 0.05. The policy implication is that educational institutions and organizations could adopt the proposed classification methodology to help learners and employees acquire the necessary knowledge and skills to use distributed systems effectively.