The Role of Azure Data Lake in Scalable and High-Performance Supply Chain Analytics
DOI:
https://doi.org/10.32628/CSEIT25112483Keywords:
Azure Data Lake, Supply Chain Analytics, Big Data, Cloud Computing, AI in Logistics, Predictive Analytics, Data Management, Machine Learning, Scalability, High Performance ComputingAbstract
Growing complexity in global supply chains demands high- performance, scalable data management capabilities. Azure Data Lake offers a cloud-based platform wherein organizations can store, process, and analyze vast volumes of structured and unstructured data in a cost-effective manner. This study discusses how Azure Data Lake elevates supply chain analytics through the convergence of big data technologies, AI, and ML models. It is a mixed-method study, with case studies and statistical analysis being used for performance improvement measurement in supply chain activities.
The main findings indicate that companies utilizing Azure Data Lake experience enhanced real-time decision-making, improved demand forecasting, and reduced inefficiencies in operations. However, challenges such as data protection and system integration persist. The paper concludes with sugges- tions for further research and strategies for optimizing Azure Data Lake utilization in supply chain analytics.
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M. Armbrust et al., ”A View of Cloud Computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010.
P. Mell and T. Grance, ”The NIST Definition of Cloud Computing,” Na- tional Institute of Standards and Technology, U.S. Dept. of Commerce, Gaithersburg, MD, USA, Special Publication 800-145, 2011.
M. Zaharia et al., ”Apache Spark: A Unified Engine for Big Data Processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.
J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, ”Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
H. Lasi, P. Fettke, H. G. Kemper, T. Feld, and M. Hoffmann, ”Industry 4.0,” Business and Information Systems Engineering, vol. 6, no. 4, pp. 239–242, 2014.
K. M. Ting,”Ensemble Machine Learning: Methods and Applications,” Data Mining and Knowledge Discovery, vol. 10, no. 2, pp. 217–254, 2012.
Sanat Talwar, Aakarsh Mavi,”AN OVERVIEW OF DNS DO- MAINS/SUBDOMAINS VULNERABILITIES SCORING FRAME-WORK”,2023.Available : https://romanpub.com/resources/Vol.
Corporation, ”Azure Data Lake Storage: Scalability and Security in Cloud Data Analytics,” 2022. [Online]. Available: https://azure.microsoft.com.
C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, ”Cloud Com- puting and Emerging IT Platforms: Vision, Hype, and Reality,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599–616, 2009.
F. Chang et al., ”Bigtable: A Distributed Storage System for Structured Data,” in Proc. 7th USENIX Symposium on Operating Systems Design and Implementation, 2006, pp. 205–218.
X. Xu, R. He, and P. Li, ”Big Data Analytics in Supply Chain Management: Trends, Strategies, and Challenges,” Journal of Business Logistics, vol. 41, no. 2, pp. 85–99, 2020.
A. Acharjya and P. K. Ahmed, ”A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 2, pp. 511–518, 2016.
S. K. Sharma and J. Gupta, ”Role of Cloud Computing in Big Data Analytics,” Journal of Cloud Computing: Advances, Systems and Ap- plications, vol. 6, no. 1, pp. 1–17, 2017.
Y. Demchenko, P. Membrey, C. Ngo, and C. De Laat, ”Big Security for Big Data: Addressing Security Challenges for the Big Data Infras- tructure,” in Proc. IEEE International Congress on Big Data (BigData Congress), 2013, pp. 64–71.
G. M. Lee, N. Crespi, J. H. Choi, and M. Boussard, ”Internet of Things,” in Encyclopedia of Cloud Computing, Springer, 2016, pp. 237–258.
B. Kitchenham, ”Procedures for Performing Systematic Reviews,” Keele University Technical Report TR/SE-0401, 2004.
P. C. Verhoef, S. A. Neslin, and B. Vroomen, ”Multichannel Customer Management: Understanding the Research Shopper Phenomenon,” Inter- national Journal of Research in Marketing, vol. 24, no. 2, pp. 129–148, 2007.
T. H. Davenport and D. J. Patil, ”Data Scientist: The Sexiest Job of the 21st Century,” Harvard Business Review, vol. 90, no. 10, pp. 70–76, 2012.
J. Manyika et al., ”Big Data: The Next Frontier for Innovation, Com- petition, and Productivity,” McKinsey Global Institute, 2011.
M. Ouzzani, H. Hammady, Z. F. Fedorowicz, and A. Elmagarmid, ”Rayyan—A Web and Mobile App for Systematic Reviews,” Systematic Reviews, vol. 5, no. 1, pp. 1–10, 2016.
A. Sheth, ”Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing,” IEEE Intelligent Systems, vol. 31, no. 2, pp. 108–112, 2016.
Sanat Talwar,”DNS Cache Snooping for Player Geolocation Risks.”(2025),availble url: https://doi.org/10.32628/CSEIT251112182
Sanat Talwar,”Integrating Threat Intelligence into Real- Time Subdomain Risk Scoring Frameworks,”(2025),availble : https://doi.org/10.32628/CSEIT25111246
Sanat Talwar,”Passive Enumeration Methodology for DNS Scan- ning in the Gaming Industry: Enhancing Security and Scala- bility,”(2025),available url: https://doi.org/10.56472/25838628/IJACT-V3I1P111
Sanat Talwar,”Evaluating Passive DNS Enumeration Tools: A Com- parative Study for Enhanced Cybersecurity in the Gaming Sec- tor,”(2025),availble url: https://doi.org/10.32628/CSEIT24106119,
Sanat Talwar,”AUTOMATED SUBDOMAIN RISK SCORING FRAMEWORK FOR REALTIME THREAT MITIGATION IN GAMING INDUSTRY,”(2024),url: https://romanpub.com/resources/Vol. Aakarsh Mavi, Sanat Talwar,”SECAUTO TOOLKIT - HARNESSING ANSIBLE FOR ADVANCED SECURITY AUTOMATION,”(2023),url :https://romanpub.com/resources/Vol.
Sanat Talwar,”SECURING CLOUD-NATIVE DNS CONFIGURA- TIONS: AUTOMATED DETECTION OF VULNERABLE S3-LINKED SUBDOMAINS,”(2022), url: https://romanpub.com/resources/Vol.
Sanat Talwar, ”DNS Tunneling in Multiplayer Games: Detection via Behavioral Analysis,”(2025),url : https://computerfraudsecurity.com/index.php/journal/article/view/410/279
Sanat Talwar,”Dynamic Just-In-Time App Servers with Automated Access Management on AWS,”(2025),url : https://computerfraudsecurity.com/index.php/journal/article/view/411/280
Sanat Talwar,”Unified Framework for Securing Cloud- Native Storage: Approach for Detecting and Mitigat- ing Multi-Cloud Bucket Misconfigurations,”(2024),url: https://computerfraudsecurity.com/index.php/journal/article/view/382/260
Sanat Talwar,”DNS over HTTPS (DoH) in Gaming: Balancing Privacy and Threat Visibility”,(2024),url: https://computerfraudsecurity.com/index.php/journal/article/view/383/261
Milankumar Rana. (2024). Fortune 1000 Companies Big Data Analytics Literature Review. Journal of Computer Science and Technology Studies, 6(2), 166-170. urlhttps://doi.org/10.32996/jcsts.2024.6.2.18
Milankumar Rana. (2024). Impact of Blockchain and Big Data on Global Economy. Journal of Computer Science and Technology Studies, 6(3), 155-158. urlhttps://doi.org/10.32996/jcsts.2024.6.3.13
Milankumar Rana. (2024). Overview of Data Warehouse Architecture, Big Data, and Green Computing. Journal of Computer Science and Technology Studies, 5(4), 213-217. urlhttps://doi.org/10.32996/jcsts.2023.5.4.22
Milankumar Rana, CHAFIK Khalid, and EL HASSANI Hajar. (2023). Disaster Recovery Plan for Business Continuity. Indian Journal of Economics and Business, 22(1).
Rana, M. (2025). A Review of the Impact of Big Data on Smart Cities. Journal of Basic and Applied Research International, 31(2), 55–64. urlhttps://doi.org/10.56557/jobari/2025/v31i29162
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