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2022 | OriginalPaper | Buchkapitel

Data Lake Versus Data Warehouse Architecture: A Comparative Study

verfasst von : Mohamed El Mehdi El Aissi, Sarah Benjelloun, Yassine Loukili, Younes Lakhrissi, Abdessamad El Boushaki, Hiba Chougrad, Safae Elhaj Ben Ali

Erschienen in: WITS 2020

Verlag: Springer Singapore

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Abstract

Each day huge quantities of data are generated from digital technologies and information systems. Therefore, processing these massive data requires a specific architecture and a good knowledge on how to handle data. Traditional databases management system can no longer be used for this type of data since they were originally designed for limited and structured data. Moreover, dedicated architecture known as Data Lake has been developed in order to extract valuable information hidden in data. The main objective of this paper is to explore the two architectures, namely, data warehouse and data lake. Furthermore, it describes the main differences and exposes key factors of each one.

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Vorheriges Kapitel Toward Moroccan Virtual University: Technical Proposal

Nächstes Kapitel Machine Learning for Credit Card Fraud Detection

https://www.mckinsey.com/businessfunctions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation. Accessed on May 2020

Haigh T (2016) How Charles Bachman invented the DBMS, a foundation of our digital world. Commun ACM 59(7):25–30CrossRef

Ariyachandra T, Watson HJ (2006) Which data warehouse architecture is most successful? Bus Intel J 11(1):4

Verma K Study of HDFS architecture and services

Nance C et al (2013) Nosql vs rdbms-why there is room for both

Zafar R et al (2016) Big data: the NoSQL and RDBMS review. In: 2016 international conference on information and communication technology (ICICTM). IEEE

Panwar A, Bhatnagar V (2020) Data Lake architecture: a new repository for data engineer. Int J Org Collective Intell (IJOCI) 10(1):63–75CrossRef

Lynch C (2008) How do your data grow? Nature 455(7209):28–29CrossRef

Rabl T et al (2012) Solving big data challenges for enterprise application performance management. arXiv preprint arXiv:1208.4167

10.

Megdiche I, Ravat F, Zhao Y (2020) A use case of data lake metadata management. Data Lakes 2:97–122CrossRef

11.

Llave MR (2018) Data lakes in business intelligence: reporting from the trenches. Procedia Comput Sci 138:516–524CrossRef

12.

Farnum, Michael A, et al (2019) A dimensional warehouse for integrating operational data from clinical trials. Database 2019

13.

Vyas S, Vaishnav P (2017) A comparative study of various ETL process and their testing techniques in data warehouse. J Stat Manage Syst 20(4):753–763

14.

Acharjya, Debi P, Ahmed K (2016) A survey on big data analytics: challenges, open research issues and tools. Int J Adv Comput Sci Appl 7.2:511–518. https://doi.org/10.14569/ijacsa.2016.070267

15.

Kakhani, Manish K, Sweeti K, Biradar SR (2015) Research issues in big data analytics. Int J Appl Innov Eng Manage 2(8):228–232

16.

Sandhu M, Purnima AS (2018) A review on Big Data analytics in business

17.

Lakshmi C, Nagendra Kumar VV (2016) Survey paper on Big Data. Int J Adv Res Comput Sci Softw Eng 6(8)

18.

Ravat F, Zhao Y (2019) Data Lakes: trends and perspectives. Int Conf Database Expert Syst Appl, Springer, Cham

19.

Chen M, Mao S, Liu Y (2014) Big data: A survey. Mobile Networks and Applications 19(2):171–209CrossRef

20.

Campbell C (2015) Top five differences between data lakes and data warehouse, January 2015. https://www.blue-granite.com/blog/bid/402596/top-five-differences-between-data-lakes-and-data-warehouses

21.

Anthony M, et al (2020) An evaluation of how Big-Data and data warehouses improve business intelligence decision making. In: World conference on information systems and technologies. Springer, Cham

22.

Gore Sumit S, Ambulgekar HP (2014) MapReduce-based warehouse systems: a survey. In: 2014 International Conference on Advances in Engineering & Technology Research (ICAETR-2014). IEEE

23.

Huang F (2015) Managing data lakes in big data era: what's a data lake and why has it became popular in data management ecosystem. In: 2015 IEEE international conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE https://doi.org/ https://doi.org/10.1109/CYBER.2015.7288049

24.

https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html, Accessed on 21/05/2020

25.

Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRef

26.

Logica B Radulescu Magdalena, using big data in the academic environment. Procd Econ Fin 33:277–286. ISSN 2212–5671, 2015. https://doi.org/10.1016/S2212-5671(15)01712-8

27.

Miloslavskaya N, Tolstoy A (2016) Big data, fast data and data lake concepts. Procedia Computer Science 88(300–305):63

28.

Umar A, Siddiqui GF (2018) Big data augmentation with Data Warehouse: a survey. In: 2018 IEEE international conference on Big Data (Big Data). IEEE

29.

Paulraj P (2004) Data warehousing fundamentals: a comprehensive guide for IT professionals. John Wiley & Sons

30.

Thibaud C, et al (2013) Big data analytics on high Velocity streams: a case study. In: 2013 IEEE international conference on Big Data. IEEE, https://doi.org/10.1109/BigData.2013.6691653

31.

Yaqoob I (2016) Big data: from beginning to future. Int J Inf Manage

32.

Abuqabita F (2019) A comparative study on big data analytics frameworks. Data resources and challenges, Canadian Center of Science and Education

33.

Labrinidis A, Jagadish HV (2012) Challenges and opportunities with big data. Proceedings of the VLDB Endowment 5(12):2032–2033CrossRef

34.

Cedrine M, Laurent A (2016) The next information architecture evolution: the data lake wave. In: Proceedings of the 8th international conference on management of digital ecosystems

Titel: Data Lake Versus Data Warehouse Architecture: A Comparative Study
verfasst von: Mohamed El Mehdi El Aissi
Sarah Benjelloun
Yassine Loukili
Younes Lakhrissi
Abdessamad El Boushaki
Hiba Chougrad
Safae Elhaj Ben Ali
Verlag: Springer Singapore
Buch: WITS 2020
Print ISBN: 978-981-336-892-7

Electronic ISBN: 978-981-336-893-4

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-981-33-6893-4_19

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