Data Lakes: Creating Versatile Architectures for Big Data Storage
Keywords:
Data Lake, Big Data Storage, Unstructured Data, Data GovernanceAbstract
Data lakes, that provide a flexible and adaptable method of storing structured, semi-structured, and unstructured data, have revolutionized big data management. When compared to conventional storage systems, they save data in its unprocessed state, which enables machine learning exploration, analysis, and application for enterprises.
References
1. Mohanty, S., Jagadeesh, M., & Srivatsa, H. (2013). Big data imperatives:
Enterprise ‘Big Data’warehouse,‘BI’implementations and analytics. Apress.
2. Pokorný, J. (2006). Database architectures: Current trends and their relationships to environmental data management. Environmental Modelling &
Software, 21(11), 1579-1586.
3. Thusoo, A., Shao, Z., Anthony, S., Borthakur, D., Jain, N., Sen Sarma, J., ... & Liu, H. (2010, June). Data warehousing and analytics infrastructure at facebook. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data (pp. 1013-1020).
4. Krafzig, D., Banke, K., & Slama, D. (2005). Enterprise SOA: service-oriented
architecture best practices. Prentice Hall Professional.
5. Cheng, Y., Qin, C., & Rusu, F. (2012, May). GLADE: big data analytics made easy. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (pp. 697-700).
6. Stankovski, V., Swain, M., Kravtsov, V., Niessen, T., Wegener, D., Kindermann, J., & Dubitzky, W. (2008). Grid-enabling data mining applications with DataminingGrid: An architectural perspective. Future Generation Computer Systems, 24(4), 259-279.
7. Bollier, D., & Firestone, C. M. (2010). The promise and peril of big data (pp. 1-
66). Washington, DC: Aspen Institute, Communications and Society Program.
8. Anwer, M. B., & Feamster, N. (2009, August). Building a fast, virtualized data plane with programmable hardware. In Proceedings of the 1st ACM workshop on
Virtualized infrastructure systems and architectures (pp. 1-8).
9. Seiler, L., Carmean, D., Sprangle, E., Forsyth, T., Abrash, M., Dubey, P., ... & Hanrahan, P. (2008). Larrabee: a many-core x86 architecture for visual computing. ACM Transactions on Graphics (TOG), 27(3), 1-15.
10. Frehner, M., & Brändli, M. (2006). Virtual database: Spatial analysis in a Web-based data management system for distributed ecological data. Environmental Modelling & Software, 21(11), 1544-1554.
11. Bieberstein, N. (2006). Service-oriented architecture compass: business value, planning, and enterprise roadmap. FT Press.
12. You, L. L., Pollack, K. T., & Long, D. D. (2005, April). Deep Store: An archival storage system architecture. In 21st International Conference on Data Engineering (ICDE'05) (pp. 804-815). IEEE.
13. Sanchez, D., Yoo, R. M., & Kozyrakis, C. (2010). Flexible architectural support for fine-grain scheduling. ACM SIGARCH Computer Architecture News, 38(1), 311-
322.
14. Delicato, F. C., Pires, P. F., Pinnez, L., Fernando, L., & Da Costa, L. F. R. (2003,
May). A flexible web service based architecture for wireless sensor networks.
In 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings. (pp. 730-735). IEEE.
15. Dean, J. (2009). Designs, lessons and advice from building large distributed
systems. Keynote from LADIS, 1.
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