Control Systems and Computers, N3, 2016, Article 4


Upr. sist. maš., 2016, Issue 3 (263), pp. 29-42.

UDC 004.7:004.75:004.9:004.738.5

A.A. Oursatyev, PhD in Techn. Sciences, Leading Research Associate, International Research and Training Centre of Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine,

Some Frameworks for Analytics Big Data

Introduction. The need to extract data from new information forces the developers of the analytic systems to pay attention on radical improvement of the traditional processing technology and to create the advanced analytics environments.

The conceptual issues of data media construction, in particular, on the Hadoop cluster system software platform is presented. The HadoopMapReduce infrastructure is described for the parallel distributed computing on the data and the evolutionary transformation of Hadoop platform using the infrastructure and streaming dynamic loads, as well as HadoopMapReduce infrastructure constraints. It is shown that an introduction of YARN (Yet Another Resource Negotiator) on the computing Hadoop platform allows to perform the different workloads in a linearly scalable cluster  Hadoop YARN  (Hadoop 2.0),
achieving calculations of the high efficiency. Frameworks, Spark, Tez and Storm use the possibility of YARN.

The components that make a total Hadoop 2.0 de facto the standard technology for working with Big Data are analyzed.

These are the constructions Hive for design-oriented interactive queries to SQL-like language HQL (Hive query language) and working with large data storage;  Pig – a high-level procedure language  Pig Latin, designed for accessing the semidistributed lennym datasets; HBase – distributed non-relational DBMS, working effectively with the individual records in real time; Apache Accumulo – oriented on a high level of safety distributed, scalable data repository with the strict requirements of the information and personal data protection.

Results. The problems of large data efficiently various types download of Hadoop ecosystem using Hive and Pig. A comparative analysis of  ELT (extract-load-transform) and  ETL (extract-transform-load) concept is presented. The first one is widely spread due to the emergence of Hadoop technology.

Download full text! (In Russian).

Keywords: Hadoop, HadoopMapReduce, Hadoop technology, advanced analytics environments.

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Received 30.03.2016