Streamlining Stock Price Analysis: Hadoop Ecosystem for Machine Learning Models and Big Data Analytics

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Author(s)

Jesslyn Noverlita 1 Herison Surbakti 2,*

1. School of ICT, Faculty of Computing and Digital Technology, HELP University, Malaysia

2. Information and Communication Technology, Rangsit University International College, Pathumthani, Thailand

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2023.05.03

Received: 23 May 2023 / Revised: 4 Jul. 2023 / Accepted: 18 Aug. 2023 / Published: 8 Oct. 2023

Index Terms

Big Data Analytics, Hadoop Ecosystem, Machine Learning, Data Warehousing, Scalability, Distributed Processing, Predictive Modeling

Abstract

The rapid growth of data in various industries has led to the emergence of big data analytics as a vital component for extracting valuable insights and making informed decisions. However, analyzing such massive volumes of data poses significant challenges in terms of storage, processing, and analysis. In this context, the Hadoop ecosystem has gained substantial attention due to its ability to handle large-scale data processing and storage. Additionally, integrating machine learning models within this ecosystem allows for advanced analytics and predictive modeling. This article explores the potential of leveraging the Hadoop ecosystem to enhance big data analytics through the construction of machine learning models and the implementation of efficient data warehousing techniques. The proposed approach of optimizing stock price by constructing machine learning models and data warehousing empowers organizations to derive meaningful insights, optimize data processing, and make data-driven decisions efficiently.

Cite This Paper

Jesslyn Noverlita, Herison Surbakti, "Streamlining Stock Price Analysis: Hadoop Ecosystem for Machine Learning Models and Big Data Analytics", International Journal of Information Technology and Computer Science(IJITCS), Vol.15, No.5, pp.25-34, 2023. DOI:10.5815/ijitcs.2023.05.03

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