Snowflake: A New Era of Cloud Data Warehousing
Keywords:
Snowflake, Cloud ComputingAbstract
Snowflake is revolutionizing data warehousing by offering a cloud-based solution that addresses the limitations of traditional on-premises systems. As businesses increasingly generate vast amounts of data, the need for scalable, flexible, and cost-effective solutions has become critical. Snowflake’s cloud-native architecture, which decouples computing and storage, enables companies to scale their data operations independently & efficiently. Unlike traditional data warehouses, Snowflake allows businesses to store structured and semi-structured data in one unified platform, making it easier to manage diverse data types. This unique design enhances performance and reduces operational costs by optimizing resources, providing a more agile data storage and analytics solution. Furthermore, Snowflake’s user-friendly interface and seamless integration with other cloud services & analytics tools empower organizations to derive meaningful insights without the complexity typically associated with traditional systems. For companies seeking to modernize their data operations, Snowflake offers the flexibility to scale up or down based on demand, a key advantage in today’s fast-paced business environment. Beyond its technical capabilities, Snowflake marks a shift in the data warehousing industry toward cloud-based solutions that offer greater agility & cost savings. It is designed to meet the growing demands for handling large-scale data, allowing businesses to leverage advanced analytics and data processing without the infrastructure constraints of legacy systems. Snowflake’s role in the cloud computing and analytics landscape is significant, as it transforms how businesses manage, store, & analyze data. By enabling organizations to scale their data operations and integrate diverse data sources more easily, Snowflake is reshaping the data warehousing industry, helping companies harness the full potential of their data in ways that were once difficult or impossible with traditional data warehouse solutions.
Downloads
References
Liu, X. (2012). Data warehousing technologies for large-scale and right-time data.
Esmail, F. S. (2014). A survey of real-time data warehouse and ETL. Management, 9(3), 3-9.
Biere, M. (2010). The new era of enterprise business intelligence: Using analytics to achieve a global competitive advantage. Pearson Education.
Vaisman, A., & Zimányi, E. (2014). Data warehouse systems. Data-Centric Systems and Applications, 9.
Wang, H., Qin, X., Zhang, Y., Wang, S., & Wang, Z. (2011, April). LinearDB: A relational approach to make data warehouse scale like MapReduce. In International Conference on Database Systems for Advanced Applications (pp. 306-320). Berlin, Heidelberg: Springer Berlin Heidelberg.
Liu, X., Thomsen, C., & Pedersen, T. B. (2011). ETLMR: a highly scalable dimensional ETL framework based on MapReduce. In Data Warehousing and Knowledge Discovery: 13th International Conference, DaWaK 2011, Toulouse, France, August 29-September 2, 2011. Proceedings 13 (pp. 96-111). Springer Berlin Heidelberg.
Adam, N. R., Atluri, V., Yu, S., & Yesha, Y. (2002, April). Efficient storage and management of environmental information. In NASA Conference Publication (pp. 165-180). NASA; 1998.
Van Der Lans, R. (2012). Data Virtualization for business intelligence systems: revolutionizing data integration for data warehouses. Elsevier.
Attasena, V., Harbi, N., & Darmont, J. (2014, November). fvss: A new secure and cost-efficient scheme for cloud data warehouses. In Proceedings of the 17th International Workshop on Data Warehousing and OLAP (pp. 81-90).
Luh, H. T. A. (2001, October). The Challenges of Managing Test: Standardization. In Proceedings International Test Conference 2001 (Cat. No. 01CH37260) (pp. 1163-1163). IEEE Computer Society.
Phipps, C., & Davis, K. C. (2002, May). Automating data warehouse conceptual schema design and evaluation. In DMDW (Vol. 2, pp. 23-32).
Araque, F., Salguero, A., & Abad, M. M. (2006). Application of data warehouse and Decision Support System in soaring site recommendation. In Information and Communication Technologies in Tourism 2006 (pp. 308-319). Springer, Vienna.
Arputhamary, B., & Arockiam, L. (2014). A review on big data integration. Int. J. Comput. Appl, 21-26.
Kapoor, B., & Sherif, J. (2012). Global human resources (HR) information systems. Kybernetes, 41(1/2), 229-238.
Kurunji, S., Ge, T., Fu, X., Liu, B., Kumar, A., & Chen, C. X. (2014). Optimizing aggregate query processing in cloud data warehouses. In Data Management in Cloud, Grid and P2P Systems: 7th International Conference, Globe 2014, Munich, Germany, September 2-3, 2014. Proceedings 7 (pp. 1-12). Springer International Publishing.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.