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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | A Research On Big Data Analytics |
| Authors Name: | K.Saileesh Kumar , P.Manideep |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_207837
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| Published Paper Id: | IJRTI2511135 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i11.207837 |
| Abstract: | Big Data Analytics (BDA) has emerged as a cornerstone of modern data-driven decision- making, enabling organizations to extract actionable insights from vast, heterogeneous datasets characterized by high volume, velocity, variety, and veracity. This article pro- vides a comprehensive review of recent advancements in BDA, highlighting its integration with artificial intelligence (AI) to address persistent challenges such as real-time process- ing, data privacy, and scalability. The research problem centers on the inefficiencies in traditional BDA pipelines when handling dynamic, unstructured data streams, which of- ten lead to delayed insights and increased operational costs. Objectives include surveying contemporary literature, proposing a hybrid methodology leveraging Apache Spark and machine learning (ML) techniques, implementing a prototype on synthetic datasets, and analyzing outcomes to demonstrate improved predictive accuracy. The literature survey reveals a surge in applications across industries, from predic- tive maintenance in manufacturing to personalized recommendations in e-commerce, yet gaps persist in seamless AI-BDA fusion for edge computing environments. The pro- posed methodology employs a four-stage pipeline: data ingestion via Kafka, distributed storage with HDFS, parallel processing using PySpark, and predictive analytics with MLlib’s random forest models. Implementation involves simulating a large-scale sales dataset (10,000 records) to forecast regional sales trends, achieving a 15% improvement in prediction error over baseline methods. Results underscore BDA’s role in boosting efficiency, with visualizations illustrating sales distributions. This work signifies BDA’s transformative potential, suggesting future enhancements in quantum-assisted analytics for ultra-high-velocity data. |
| Keywords: | Big Data Analytics, Artificial Intelligence Integration, Predictive Mainte- nance, Cloud Computing Challenges, Spark Framework, Machine Learning Applications |
| Cite Article: | "A Research On Big Data Analytics ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.b286-b290, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511135.pdf |
| Downloads: | 000227 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2511135
Registration ID:207837
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i11.207837
Page No: b286-b290 Country: Enathur , vedal, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511135 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511135 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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