AI-Driven Cloud-Based Data Lakes for Intelligent Enterprise Analytics and Reporting
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706028Keywords:
AI-powered data lakes, Cloud data architecture, Intelligent analytics platforms, Big data processing, Machine learning pipelines, Data lakehouse architecture, Real-time data analytics, Enterprise data management, Predictive analytics systems, Data governance and security, Scalable cloud storage, ETL/ELT automation, Business intelligence (BI) integration, Advanced data visualization, Automated reporting systemsAbstract
Two significant developments have converged: the emergence of cloud-based data lakes, conducive to cost-effective data storage and processing for intelligent enterprise applications, and the application of artificial intelligence (AI) to greatly improve the various processes required to build and maintain a data lake. Intelligent enterprise applications can be categorized as descriptive analytics and dashboards; advanced analytics and predictive modeling; and real-time analytics and streaming data. These categories map to typical application areas such as customer analytics and personalization; operational intelligence and asset monitoring; and supply chain optimization and risk management. The primary issues in building a cloud-based data lake include data privacy and sovereignty; fairness, bias and explainability in AI; performance management and cost optimization.The abstract now conforms with the rest of the paper.
Data governance and cataloging; security and compliance; architecture and components are addressed. Emerging AI technologies significantly enhance the intelligent enterprise applications in a cloud-based data lake, particularly data ingestion and feature engineering; automated metadata enrichment and lineage; and data quality and cleansing. Supporting both the descriptive and advanced analytics categories of intelligent enterprise applications, the Intelligent Enterprise Analytics and Reporting Framework comprises descriptive analytics, advanced analytics, real-time analytics and streaming data; integrating the domains of customer analytics and personalization; operational intelligence and asset monitoring; supply chain optimization and risk management.
References
[1] Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
[2] Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
[3] Singireddy, J. (2024). AI-Driven Payroll Systems: Ensuring Compliance and Reducing Human Error. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
[4] Inala, R., & Somu, B. (2024). Agentic AI in Retail Banking: Redefining Customer Service and Financial Decision-Making. Journal of Artificial Intelligence and Big Data Disciplines, 1(1).
[5] Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
[6] Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29 (4), 5493–5505.
[7] Singireddy, S. (2024). The Integration of AI and Machine Learning in Transforming Underwriting and Risk Assessment Across Personal and Commercial Insurance Lines. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 3966-3991.
[8] Pamisetty, A., Adusupalli, B., Mashetty, S., & Singreddy, S. (2024). Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management. Sneha, Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management (December 05, 2024).
[9] Sheelam, G. K. (2024). Deep Learning-Based Protocol Stack Optimization in High-Density 5G Environments. European Advanced Journal for Science & Engineering (EAJSE)-p-ISSN, 3050-9696.
[10] Singireddy, J. (2023). Finance 4.0: Predictive analytics for financial risk management using AI. European Journal of Analytics and Artificial Intelligence (EJAAI) p-ISSN, 3050-9556.
[11] Meda, R. (2024). Enhancing Paint Formula Innovation Using Generative AI and Historical Data Analytics. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
[12] Kummari, D. N. (2023). AI-powered demand forecasting for automotive components: A multi-supplier data fusion approach. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN, 3050-9734.
[13] Singireddy, J. (2024). Deep Learning Architectures for Automated Fraud Detection in Payroll and Financial Management Services: Towards Safer Small Business Transactions. Journal of Artificial Intelligence and Big Data Disciplines, 1(1), 75-85.
[14] Nandan, B. P. (2024). Semiconductor Process Innovation: Leveraging Big Data for Real-Time Decision-Making. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4038-4053.
[15] Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20. https://doi.org/10.31586/jaibd.2021.1361
[16] Singreddy, S. (2024). Applying deep learning to mobile home and flood insurance risk evaluation. Available at SSRN 5238946.
[17] Nandan, B. P. (2024). Revolutionizing Semiconductor Chip Design through Generative AI and Reinforcement Learning: A Novel Approach to Mask Patterning and Resolution Enhancement. International Journal of Medical Toxicology and Legal Medicine, 27(5), 759-772.
[18] Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
[19] Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
[20] Inala, R. (2022). Engineering Data Products for Investment Analytics: The Role of Product Master Data and Scalable Big Data Solutions. International Journal of Scientific Research and Modern Technology, 155-171.
[21] Recharla, M. (2024). Advances in Therapeutic Strategies for Alzheimer’s Disease: Bridging Basic Research and Clinical Applications. American Online Journal of Science and Engineering (AOJSE)(ISSN: 3067-1140), 2(1).
[22] Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).
[23] Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
[24] Reddy Segireddy, A. (2024). Federated Cloud Approaches for Multi-Regional Payment Messaging Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 442-450.
[25] Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
[26] Pamisetty, V. (2024). AI-Driven Decision Support for Taxation and Unclaimed Property Management: Enhancing Efficiency through Big Data and Cloud Integration. Available at SSRN 5250776.
[27] Mahesh Recharla, “Integrated Genomic and Neurobiological Pathway Mapping for Early Detection of Alzheimer’s Disease,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2023.12122.
[28] Mangalampalli, B. M. (2024). AI-Enhanced Data Governance: Automating Compliance In Healthcare Analytics Platforms. The Review of Diabetic Studies, 191-204.
[29] Pamisetty, V. (2024). Transforming taxation systems through predictive analytics and AI-driven compliance monitoring tools. Am Data Sci J Adv Comput, 3, 55-68.
[30] Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.
[31] Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
[32] Meda, R. (2023). Data Engineering Architectures for Scalable AI in Paint Manufacturing Operations. European data science journal.
[33] Pamisetty, V., & Amistapuram, K. Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning.
[34] Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security.
[35] Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Available at SSRN 5532601.
[36] Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.
[37] Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
[38] Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN 5697663.
[39] Sheelam, G. K., & Koppolu, H. K. R. (2024). From Transistors to Intelligence: Semiconductor Architectures Empowering Agentic AI in 5G and Beyond. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 4518-4537.
[40] Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
[41] Meda, R. (2024). Predictive Maintenance of Spray Equipment Using Machine Learning in Paint Application Services. European Data Science Journal (EDSJ) p-ISSN, 3050-9572.
[42] Sheelam, G. K. (2024). Towards autonomic wireless systems: integrating agentic AI with advanced semiconductor technologies in telecommunications. Am. Online J. Sci. Eng., 3(4), 234-256.
[43] Pamisetty, A. (2024). Leveraging Agentic AI and Cloud Infrastructure for Predictive Logistics in National Food Supply Chains. Available at SSRN 5262994.
[44] Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
[45] Deep Learning-Driven Optimization of ISO 20022 Protocol Stacks for Secure Cross-Border Messaging. (2024). MSW Management Journal, 34(2), 1545-1554.
[46] Pamisetty, A. (2024). Leveraging Big Data Engineering for Predictive Analytics in Wholesale Product Logistics. Available at SSRN 5231473.
[47] Nagabhyru, K. C. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898-5910.
[48] Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology (IJSRMT).
[49] Kolla, S. H. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10, 495-506.
[50] Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
[51] Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research , 101–122. https://doi.org/10.63278/jicrcr.vi.3738
[52] Bandi, V. D. V. K. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13.
[53] Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
[54] Kolla, S. H. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications, 31(4).
[55] Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
[56] Kolla, S. K. (2024). Federated Machine Learning On Big Healthcare Data For Privacy-Preserving Analytics. The Review of Diabetic Studies, 175-190.
[57] Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
[58] Singh, D., Tripathi, G., & Jara, A. J. (2019). A survey of Internet-of-Things: Future vision, architecture, challenges, and services. IEEE Internet of Things Journal, 1(1), 796–803.
[59] Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
[60] Kolla, S. H. (2024). RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 476-486.
[61] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[62] Kolla, S. K. (2023). Big Data–Driven Machine Learning Frameworks for Clinical Risk Prediction. International Journal of Medical Toxicology and Legal Medicine, 26(3), 44-59.
[63] Davuluri, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking.
[64] Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
[65] Amistapuram, K. (2024). Federated Learning for Cross-Carrier Insurance Fraud Detection: Secure Multi-Institutional Collaboration. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6727-6738.
[66] Kolla, T. (2024). AI-Powered Data Catalog Systems For Healthcare Data Discovery And Governance. South Eastern European Journal of Public Health, 2296–2311. https://doi.org/10.70135/seejph.vi.7077
[67] Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
[68] Amistapuram, K. (2024). Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning. Available at SSRN 6143426.
[69] Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
