AI-Driven Data Integration in Multi-Cloud Environments

Since more organizations are moving to multi-cloud and the need of the hour is to standardize and holistically bring together data from across those platforms this issue is taking an increasing precedence.In this paper, we explore in depth how the Application of Artificial Intelligence (AI) can solve data accuracy problems.We also deal with Data integration on multi-cloud underpinnings like machine view of ML (machine learning), Scalability, Natural Language Processing (NLP), Generative AI, etc.These would eventually be the ways in which AI-based cloud-data replication and generative models can be utilized to illustrate recent progress, described as an exemplary use case.Through the lens of case studies and hands-on labs, we see how AI could impact data interoperability and fidelity between multi-cloud configurations [2][3] as there are few use cases as highlighted below which will be covering the detailing in our research.

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Publication Details

Journal
International Journal of Global Innovations and Solutions (IJGIS)
Published
2025-02-01
DOI
https://doi.org/10.21428/e90189c8.0456763f
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Driven Data Integration in Multi-Cloud Environments

Nilesh Jain, Shalmali Patil, Satish Waybhase
International Journal of Global Innovations and Solutions (IJGIS)
Big Data and Business Intelligence
article

AI-Driven Data Integration in Multi-Cloud Environments

Nilesh Jain, Shalmali Patil, Satish Waybhase
article en

Abstract

Since more organizations are moving to multi-cloud and the need of the hour is to standardize and holistically bring together data from across those platforms this issue is taking an increasing precedence.In this paper, we explore in depth how the Application of Artificial Intelligence (AI) can solve data accuracy problems.We also deal with Data integration on multi-cloud underpinnings like machine view of ML (machine learning), Scalability, Natural Language Processing (NLP), Generative AI, etc.These would eventually be the ways in which AI-based cloud-data replication and generative models can be utilized to illustrate recent progress, described as an exemplary use case.Through the lens of case studies and hands-on labs, we see how AI could impact data interoperability and fidelity between multi-cloud configurations [2][3] as there are few use cases as highlighted below which will be covering the detailing in our research.

International Journal of Global Innovations and Solutions (IJGIS)
The New World Foundation (US)
Openalex Percentile: Top 98%
Big Data and Business Intelligence
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