International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 2
March-April 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Comparative Analysis Of Data Lakes And Data Warehouses For Machine Learning

Author(s) Bhanu Prakash Reddy Rella
Country United States
Abstract The selection of the best data storage and management system stands essential because machine learning (ML) continues expanding its industry-driven innovation. Today's two most prevalent large-scale data processing systems are data lakes and data warehouses, which provide unique strengths and barriers when applied to ML workloads. This paper thoroughly compares data lakes and data warehouses by Analyzing their operational speed, abilities, and price efficiency alongside data, management controls, and ML integration capabilities.
Data lakes showcase their superiority in processing unstructured together with semi-structured information because they serve deep learning and big data analytics requirements. A data warehouse offers optimized querying and structured storage, which is suitable for traditional business intelligence applications and ML platforms. The execution speed of data warehouses is faster compared to data lakes, but data lakes enable enhanced real-time abilities and flexibility for large-scale ML work.
The research approach consists of conducting a feature-based examination of both architectures, combined with real-world examples, performance scaling data, and cost measurements. This research finds that AI analytics operate most successfully through data lakes; however, structured ML jobs need data warehouses for efficient operation. The combination of data lakehouse technology presents a new possibility for joining both paradigms to create more efficient environments for machine learning applications.
Keywords Data Lakes, Data Warehouses, Machine Learning Workloads, Big Data Analytics, Data Storage Architectures, Lakehouse Architecture, AI-optimized databases.
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-03-13
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.38869
Short DOI https://doi.org/g8938x
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com