International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

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

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
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

Optimizing Multi-Team Data Sharing: Evaluating Shared Schemas versus Data Lakes

Author(s) Naveen Edapurath Vijayan
Country United States
Abstract In today's data-driven organizations, multiple teams often require access to shared datasets for various analytical and operational purposes. Designing data architectures that facilitate efficient data sharing while maintaining performance, security, and cost-effectiveness is a significant challenge. This paper examines the trade-offs between two architectural approaches for data sharing among multiple teams: using a single Amazon Redshift cluster with shared schemas and controlled access, and implementing a data lake architecture where teams own their compute resources but share data through granted access. By analyzing factors such as performance, scalability, cost efficiency, data governance, security, and team autonomy, the paper provides insights into optimizing data infrastructure for organizations. A case study illustrates practical considerations and outcomes of both approaches. Recommendations are offered to guide organizations in selecting the most suitable architecture for their needs.
Keywords Amazon Redshift, Data Lake, Data Architecture, Data Sharing, Multi-Team Collaboration, Cloud Computing, Data Governance, Scalability, Cost Efficiency
Field Computer > Data / Information
Published In Volume 10, Issue 4, October-December 2019
Published On 2019-12-04
Cite This Optimizing Multi-Team Data Sharing: Evaluating Shared Schemas versus Data Lakes - Naveen Edapurath Vijayan - IJSAT Volume 10, Issue 4, October-December 2019. DOI 10.5281/zenodo.14474365
DOI https://doi.org/10.5281/zenodo.14474365
Short DOI https://doi.org/g8vmn8
View / Download PDF File

Share this



CrossRef

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

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

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 IJSAT
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 Message on WhatsApp +91-9687-182-185 editor@ijsat.org