Data Lakes: Building Flexible Architectures for Big Data Storage
Keywords:
Data Lake, Unstructured Data, ScalabilityAbstract
Data lakes are emerging as a powerful solution for managing big data's growing volume, variety, and velocity. Unlike traditional data storage systems, data lakes provide a flexible and scalable architecture capable of storing vast amounts of structured, semi-structured, and unstructured data. This approach allows organizations to store data in its raw form, providing a more agile environment for data exploration, analytics, and machine learning. Data lakes support modern big data technologies, enabling organizations to leverage real-time data processing and gain deeper insights from diverse data sources. The architecture of a data lake is designed to accommodate the complexity of big data workloads, providing the flexibility to integrate with various data management tools, analytics platforms, and cloud-based services. However, with the potential benefits come challenges, particularly around data governance, security, and ensuring data quality. In this context, effective data management practices are essential to avoid data silos and ensure that data lakes deliver on their promise of transforming business intelligence. This paper explores the fundamental principles and best practices for building data lakes, highlighting how they can be optimized for ample data storage and how organizations can successfully navigate the challenges associated with their implementation. By providing an efficient framework for data management and analysis, data lakes are helping organizations unlock the full potential of their big data, enabling more intelligent decision-making and fostering innovation across industries.
Downloads
References
Mohanty, S., Jagadeesh, M., & Srivatsa, H. (2013). Big data imperatives:
Enterprise ‘Big Data’warehouse,‘BI’implementations and analytics. Apress.
Pokorný, J. (2006). Database architectures: Current trends and their relationships to environmental data management. Environmental Modelling &
Software, 21(11), 1579-1586.
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).
Krafzig, D., Banke, K., & Slama, D. (2005). Enterprise SOA: service-oriented
architecture best practices. Prentice Hall Professional.
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).
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.
Bollier, D., & Firestone, C. M. (2010). The promise and peril of big data (pp. 1-
. Washington, DC: Aspen Institute, Communications and Society Program.
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).
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.
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.
Bieberstein, N. (2006). Service-oriented architecture compass: business value, planning, and enterprise roadmap. FT Press.
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.
Sanchez, D., Yoo, R. M., & Kozyrakis, C. (2010). Flexible architectural support for fine-grain scheduling. ACM SIGARCH Computer Architecture News, 38(1), 311-
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.
Dean, J. (2009). Designs, lessons and advice from building large distributed
systems. Keynote from LADIS, 1.
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.
