MLlib: Machine Learning in Apache Spark
N. Deshai1, B.V.D.S.Sekhar2, S. Venkataramana3
1N. Deshai, Department of Information Technology, Sagi Ramakrishnam Raju Engineering College, Bhimavaram (Andhra Pradesh), India.
2B.V.D.S.Sekhar, Department of Information Technology, Sagi Ramakrishnam Raju Engineering College, Bhimavaram (Andhra Pradesh), India.
3S. Venkata Ramana, Department of Information Technology, Sagi Ramakrishnam Raju Engineering College, Bhimavaram (Andhra Pradesh), India.
Manuscript received on 11 May 2019 | Revised Manuscript received on 05 June 2019 | Manuscript Published on 15 June 2019 | PP: 45-49 | Volume-8 Issue-1S3 June 2019 | Retrieval Number: A10090681S319/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: In latest digital era, big data ensure tremendously process different data streams, which must paying attention in different areas of computer science. In today’s digital world, Apache Spark is a latest, lightning-fast, most popular and widely used more successful data processing engine to significantly process large-scale real-world datasets. In addition, which is extremely well suited for incremental machine learning activities, tremendously use in several statistical computations to transform a diversity of complex sources of data turn in to more knowledge and facts, also offering top abilities for relevant pattern exploration. MLlib could be an influential tool for enormous data analytics, providing great features to various machine-learning functions varying like regression, categorization to cluster and rule based extraction. In this paper, describes MLlib, evaluate the central open-source paradigm Apache Spark, core technology and operate a decentralized system study library for spark. Explicitly, in this paper, we conduct multiple tests with real-world machine learning to analyze the platforms subjective and objective characteristics.
Keywords: Big Data, Machine Learning, Apache Spark, MLlib.
Scope of the Article: Machine Learning