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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21717
Article Published : 8562
Total Authors : 22518
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| Paper Title: | Cohesive Data Workflows: A Unified Framework for AWS and GCP |
| Authors Name: | Praveen Kodakandla |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206811
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| Published Paper Id: | IJRTI2111014 |
| Published In: | Volume 6 Issue 11, November-2021 |
| DOI: | |
| Abstract: | The widespread adoption of cloud computing has encouraged organizations to design data architectures that span several cloud providers, enhancing flexibility, resilience, and innovation in how data is processed and secured. This shift has accelerated the movement toward cohesive data ecosystems, where companies architect unified data operations that operate seamlessly across AWS, Google Cloud, and other providers. A well-structured multi-cloud approach minimizes vendor dependency, optimizes cost-to-performance ratios, and ensures compliance with global data regulations. However, this model also introduces challenges—such as harmonizing diverse data formats, maintaining consistent security postures, synchronizing pipelines, and enabling unified observability. This article explores the frameworks, tools, and design principles essential for cross-cloud big data engineering. It highlights how core services like Amazon S3, AWS Glue, GCP BigQuery, and DataProc can be integrated to build distributed, scalable, and governed pipelines. Through an applied use case, it demonstrates how enterprises can implement fault-tolerant, cost-efficient, and AI-enabled pipelines that unify disparate environments into a single logical data fabric. The discussion concludes with an overview of emerging trends such as cloud-agnostic orchestration layers, generative-AI-driven data governance, and intelligent workload optimization—cornerstones of tomorrow’s unified data architectures. |
| Keywords: | Cohesive Data Workflows: A Unified Framework for AWS and GCP |
| Cite Article: | "Cohesive Data Workflows: A Unified Framework for AWS and GCP", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.6, Issue 11, page no.68-78, November-2021, Available :http://www.ijrti.org/papers/IJRTI2111014.pdf |
| Downloads: | 000191 |
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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 |
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Published Paper ID: IJRTI2111014
Registration ID:206811
Published In: Volume 6 Issue 11, November-2021
DOI (Digital Object Identifier):
Page No: 68-78 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2111014 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2111014 |
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ISSN: 2456-3315
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