Big Data Tools and Techniques: A Roadmap for Predictive Analytics
Ritu Ratra1, Preeti Gulia2
1Ms RItu Ratra, Research Scholar, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana.
2Dr. Preeti Gulia, Assistant Professor, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana.
Manuscript received on February 01, 2019. | Revised Manuscript received on February 14, 2019. | Manuscript published on December 30, 2019. | PP: 4986-4996 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2360129219/2019©BEIESP | DOI: 10.35940/ijeat.B2360.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, large volume of data is generated in the form of text, voice, video, images and sound. It is very challenging job to handle and to get process these different types of data. It is very laborious process to analysis big data by using the traditional data processing applications. Due to huge scattered file systems, a big data analysis is a difficult task. So, to analyses the big data, a number of tools and techniques are required. Some of the techniques of data mining are used to analyze the big data such as clustering, prediction, and classification and decision tree etc. Apache Hadoop, Apache spark, Apache Storm, MongoDB, NOSQL, HPCC are the tools used to handle big data. This paper presents a review and comparative study of these tools and techniques which are basically used for Big Data analytics. A brief summary of tools and techniques is represented here.
Keywords: Big data, Clustering, Hadoop, Spark, MongoDB, HDFS.