Next-Generation Data Warehousing: Advances in serverless designs and cloud-native data collections
Keywords:
Cloud-native data warehouse, serverless architecture, big dataAbstract
The way that organizations store, process & analyzes the data is had being revolutionized by the next-generation data warehousing, which is moving towards the serverless & cloud-native architectures. With the flexibilities & scalability to manage massive data volumes without the constraints of on-premises hardware, cloud-native warehouses are designed for the cloud. This enables the companies to grow as required & does away them with the need for ongoing hardware updates. This is further enhanced by serverless designs, which do away with the needs of server management completely. Billing is based on real consumptions rather than the pre-provisioned capacities & the resources are automatically assigned. This increases the agility & cost efficiency by allowing the data professionals to concentrate on analysis & insights generation rather than the infrastructure management. These developments are perfect for contemporary, fast-paced applications as they enable actual time data processing. Without having to make the huge infrastructure expenditures of the past, businesses can now make choices more quickly than before & intelligently. Serverless & cloud-native data warehousing reduces costs & complexity, allowing businesses to stay competitive in a data-driven environment & adjust to changing the data requirements.
References
1. Laszewski, T., Arora, K., Farr, E., & Zonooz, P. (2018). Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud. Packt Publishing Ltd.
2. NETTO, M. A., TOOSI, A. N., RODRIGUEZ, M. A., LLORENTE, I. M., DI VIMERCATI, S. D. C., SAMARATI, P., ... & SHEN, H. (2018). A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade.
3. Tech, B. (2015). Cloud Computing. SlideShare Site: https://www. slideshare. net/ranjanravi33/cloud-computing-46478251.
4. Stodder, D. (2018). BI and Analytics in the Age of AI and Big Data. TWDI Best Practices Report.
5. Kousalya, G., Balakrishnan, P., & Raj, C. P. (2017). Automated workflow scheduling in self-adaptive clouds: Concepts, algorithms and methods. Springer.
6. Samwel, B., Cieslewicz, J., Handy, B., Govig, J., Venetis, P., Yang, C., ... & Venkataraman, S. (2018). F1 query: Declarative querying at scale. Proceedings of the VLDB Endowment, 11(12), 1835-1848.
7. Ramos Chavez, R. (2018). Towards network based media processing using cloud technologies.
8. Fox, G. C., Ishakian, V., Muthusamy, V., & Slominski, A. (2017). Status of serverless computing and function-as-a-service (faas) in industry and research. arXiv preprint arXiv:1708.08028.
9. Inmon, W. H., Strauss, D., & Neushloss, G. (2010). DW 2.0: The architecture for the next generation of data warehousing. Elsevier.
10. Russom, P. (2009). Next generation data warehouse platforms. TDWI Best Practices Report: fourth quarter.
11. Garcelon, N., Neuraz, A., Salomon, R., Bahi-Buisson, N., Amiel, J., Picard, C., ... & Rance, B. (2018). Next generation phenotyping using narrative reports in a rare disease clinical data warehouse. Orphanet journal of rare diseases, 13, 1-11.
12. Thiele, H., Glandorf, J., Hufnagel, P., Körting, G., & Blüggel, M. (2008). Managing proteomics data: from generation and data warehousing to central data repository. J Proteomics Bioinform, 1, 485-507.
13. Abrahiem, R. (2007, May). A new generation of middleware solutions for a near-real-time data warehousing architecture. In 2007 IEEE International Conference on Electro/Information Technology (pp. 192-197). IEEE.
14. Inmon, B. (2006). DW 2.0; Architecture for the Next Generation of Data Warehousing. Information Management, 16(4), 8.
15. Kelly, S. (2007). Data warehousing in action. John Wiley & Sons.
16. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
17. Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
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