Loading Now

Leveraging AWS Serverless Computing and Snowflake Data Sharing for Optimal Cost-Efficiency in Data-Intensive Workloads

International Journal of Computer Techniques – Volume 11 Issue 4, August 2024 | ISSN 2394-2231

Kishore Gade, Shamee M
Vice President, Lead Software Engineer at JPMorgan Chase, USA
Email: kishore.gade@jpmchase.com
Cloud Data Architect, Columbus, Ohio, USA
Email: shamee.m@example.com

Abstract

Enterprises struggle more & more in the present dynamic cloud environments to balance performance, scalability & the cost-effectiveness for data-intensive operations. With Snowflake’s data-sharing features, AWS serverless computing offers a strong synergies to improve infrastructure efficiency while keeping agility & the speed. By eliminating the need for server installation & the administration, AWS serverless services—including AWS Lambda, Amazon API Gateway & the AWS Step Functions—let businesses focus more on innovation than on infrastructure maintenance. By means of seamless, secure & the actual time data exchange between teams & the companies, Snowflake’s data-sharing method helps to substantially reduce storage & the processing costs by eliminating data duplication or relocation. Combining AWS serverless technologies with Snowflake helps businesses to create a cost-effective architecture that dynamically extends based on workload needs while maintaining data governance and security. This article investigates how using this synergy might provide operational efficiency, reduced overhead, and accelerated data-driven decision-making. We review ideal methods, useful applications, and implementation strategies that improve cost effectiveness while guaranteeing outstanding availability and performance. To enable smooth deployment, we also address potential challenges such data pipeline improvements, security concerns, and monitoring. In the end, readers will fully understand how Snowflake data sharing and AWS serverless computing might transform their handling of significant data workloads and improve cloud expenditure economy.

Keywords

AWS Serverless · Snowflake · Data Sharing · Cost Optimization · Cloud Computing · Data Architecture · Serverless Analytics · Data-Intensive Workloads · AWS Lambda · Amazon S3 · Data Pipeline Efficiency · Pay-as-You-Go · Cloud Cost Management · Elastic Scaling · Multi-Cloud Data Sharing · Event-Driven Computing · Data Lake Integration · ETL Modernization · Scalable Data Processing · API-Driven Workflows · Data Governance · Real-Time Data Processing · Cloud-Native Storage · Serverless Data Pipelines · Consumption-Based Pricing · Automated Resource Scaling · Secure Data Exchange

References

  1. Mozafari, Barzan, et al. “Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning.” Companion of the 2023 International Conference on Management of Data. 2023.
  2. Klimovic, Ana, et al. “Pocket: Elastic ephemeral storage for serverless analytics.” 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 2018.
  3. Lin, Jiaxin, et al. “Towards Accelerating Data Intensive Application’s Shuffle Process Using SmartNICs.” Proceedings of the ACM on Measurement and Analysis of Computing Systems 7.2 (2023): 1-23.
  4. Winter, Christian, et al. “On-demand state separation for cloud data warehousing.” Proceedings of the VLDB Endowment 15.11 (2022): 2966-2979.
  5. Klimovic, A. (2019). Fast, Elastic Storage for the Cloud. Stanford University.
  6. Wang, Ruihong, et al. “dLSM: An LSM-based index for memory disaggregation.” 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023.
  7. Hossain, Md Saber. Design and Implementation of Serverless Architecture for i2b2 on AWS Cloud and Snowflake Data Warehouse. MS thesis. University of Missouri-Columbia, 2023.
  8. Kashyap, Ravi. “Data Sharing, Disaster Management, and Security Capabilities of Snowflake a Cloud Datawarehouse.” International Journal of Computer Trends and Technology 71.2 (2023): 78-86.
  9. Pillai, V. (2021). Implementing Efficient Data Operations: An Innovative Approach.
  10. Seenivasan, Dhamotharan. “Optimizing Cloud Data Warehousing: A Deep Dive into Snowflakes Architecture and Performance.” International Journal of Advanced Research in Engineering and Technology 12.3 (2021).
  11. George, A. Shaji. “Deciphering the Path to Cost Efficiency and Sustainability in the Snowflake Environment.” Partners Universal International Innovation Journal 1.4 (2023): 231-250.
  12. Lekkala, Chandrakanth. “Cloud-Based Data Warehousing Optimization Techniques.” Journal of Scientific and Engineering Research 9.5 (2022): 114-118.
  13. L’Esteve, Ron C. “Decentralizing Data and Democratizing Analytics.” The Cloud Leader’s Handbook: Strategically Innovate, Transform, and Scale Organizations. Berkeley, CA: Apress, 2023. 79-104.
  14. Selvarajan, Guru Prasad. “OPTIMISING MACHINE LEARNING WORKFLOWS IN SNOWFLAKEDB: A COMPREHENSIVE FRAMEWORK SCALABLE CLOUD-BASED DATA ANALYTICS.” Technix International Journal for Engineering Research 8 (2021): a44-a52.
  15. Morton, Adam. “Developing Applications in Snowflake.” Mastering Snowflake Solutions: Supporting Analytics and Data Sharing. Berkeley, CA: Apress, 2022. 201-219.
  16. Piyushkumar Patel, and Deepu Jose. “Preparing for the Phased-Out Full Expensing Provision: Implications for Corporate Capital Investment Decisions ”. Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, May 2023, pp. 699-18
  17. Piyushkumar Patel. “Accounting for Climate-Related Contingencies: The Rise of Carbon Credits and Their Financial Reporting Impact”. African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 1, June 2023, pp. 490-12
  18. Piyushkumar Patel. “The Role of Central Bank Digital Currencies (CBDCs) in Corporate Financial Strategies and Reporting”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, Sept. 2023, pp. 1194-1
  19. Sairamesh Konidala, et al. “The Role of IAM in Preventing Cyberattacks ”. African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 1, Feb. 2023, pp. 538-60
  20. Sairamesh Konidala, and Guruprasad Nookala. “Real-Time Analytics for Enhancing Customer Experience in the Payment Industry”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 1, Apr. 2023, pp. 950-68
  21. Ravi Teja Madhala, and Sateesh Reddy Adavelli. “Blockchain for Fraud Detection in P&C Insurance Claims”. Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, Jan. 2023, pp. 740-66
  22. Ravi Teja Madhala. “Artificial Intelligence for Predictive Underwriting in P&C Insurance”. African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 1, Mar. 2023, pp. 513-37
  23. Ravi Teja Madhala, et al. “Cybersecurity Risk Modeling in P&C Insurance”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 1, Mar. 2023, pp. 925-49
  24. Sarbaree Mishra. “Incorporating Automated Machine Learning and Neural Architecture Searches to Build a Better Enterprise Search Engine”. African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 2, Dec. 2023, pp. 507-2
  25. Sarbaree Mishra, et al. “Hyperfocused Customer Insights Based On Graph Analytics And Knowledge Graphs”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, Oct. 2023, pp. 1172-93.
  26. Sarbaree Mishra, and Jeevan Manda. “Building a Scalable Enterprise Scale Data Mesh With Apache Snowflake and Iceberg”. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, June 2023, pp. 695-16.
  27. Pamulaparthyvenkata, Saigurudatta, et al. “Utilizing EHR in Machine Learning-Based Systems for Early Heart Disease Prediction in Healthcare Applications.” 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024.
  28. Kathiriya, S., Nuthakki, S., Mulukuntla, S., & Charllo, B. V. (2023). AI and The Future of Medicine: Pioneering Drug Discovery with Language Models. International Journal of Science and Research, 12(3), 1824-1829.
  29. Boppana, Venkat Raviteja. “Data Analytics for Predictive Maintenance in Healthcare Equipment.” EPH-International Journal of Business & Management Science 9.2 (2023): 26-36.
  30. Boppana, Venkat Raviteja. “AI Integration in CRM Systems for Personalized Customer Experiences.” Available at SSRN 4987149 (2023).
  31. Komandla, Vineela. “Enhancing User Experience in Fintech: Best Practices for Streamlined Online Account Opening.” Educational Research (IJMCER) 2.4 (2018): 01-08.
  32. Komandla, Vineela. “Transforming Customer Onboarding: Efficient Digital Account Opening and KYC Compliance Strategies.” Available at SSRN 4983076 (2018).
  33. Boda, V. V. R., and H. Allam. “Scaling Kubernetes for Healthcare: Real Lessons from the Field.” Innovative Engineering Sciences Journal 3.1 (2023).
  34. Boda, V. V. R. “What’s Next for Infrastructure.” The Future of Code-Driven Healthcare. MZ Computing Journal 4.2 (2023).

About the Authors

Kishore Gade
Vice President, Lead Software Engineer at JPMorgan Chase, USA
Email: kishore.gade@jpmchase.com

Shamee M
Cloud Data Architect, Columbus, Ohio, USA
Email: shamee.m@example.com

International Journal of Computer Techniques – Volume 11 Issue 4, August 2024 | ISSN 2394-2231

Leveraging AWS Serverless Computing and Snowflake Data Sharing for Optimal Cost-Efficiency in Data-Intensive WorkloadsDownload