Big data: modern approaches to storage and analysis

Abstract

Big data challenged traditional storage and analysis systems in several new ways. In this paper we try to figure out how to overcome this challenges, why it's not possible to make it efficiently and describe three modern approaches to big data handling: NoSQL, MapReduce and real-time stream processing. The first section of the paper is the introduction. The second section discuss main issues of Big Data: volume, diversity, velocity, and value. The third section describes different approaches to solving the problem of Big Data. Traditionally one might use a relational DBMS. The paper propose some steps that allow to continue RDBMS using when it’s capacity becomes not enough. Another way is to use a NoSQL approach. The basic ideas of the NoSQL approach are: simplification, high throughput, and unlimited scaling out. Different kinds of NoSQL stores allow to use such systems in different applications of Big Data. MapReduce and it’s free implementation Hadoop may be used to provide scaling out Big Data analytics. Finally, several data management products support real time stream processing under Big Data. The paper briefly overviews these products. The final section of the paper is the conclusion.

Keywords

• большие данные
• реляционная модель
• nosql
• mapreduce
• hadoop
• s4
• storm