Data warehouse and data lake as components of the information technology platform of the smart region “Center of Europe”

The article analyzes modern approaches to the use of data warehouses and data lakes in the construction of information technology platforms for smart regions. Data processing technologies in data warehouses and data lakes allow for the integration, storage, and analysis of large amounts of information generated by various sources, including operational transaction systems, IoT sensors, and other data received in real time. The effective use of data warehouses opens up opportunities to improve the quality of regional management, optimize the work of all services, and raise the standard of living of the population.
The creation of information and technology platforms for smart regions using data warehouses and data lakes is a key direction in the development of modern information technologies, allowing them to be used effectively not only in densely populated cities, but also in areas with complex geography, multinational structures, and diverse economic sectors, such as Transcarpathia. The article discusses the features of building data warehouses and data lakes as components of an information system-from the level of operational processing to the creation of data showcases that provide localized access to information for specific areas of implementation, in particular for the State Emergency Service of Transcarpathia.

  1. O. Palka, N. Kunanets, V. Pasichnyk, O. Matsiukand S. Matsiuk, "Comparative Analysis of Smart City Platforms," CEUR Workshop Proceedings, vol. 3403, pp. 487–499, 2023. [Online]. Available: https://ceur-ws.org/
  2. J. Colding, M. Colding, and S. Barthel, "The smart city model: A new panacea for urban sustainability or unmanageable complexity," Environment and Planning B: Urban Analytics and City Science, vol. 47, no. 1, pp. 179–187, 2020. [Online]. Available:
    http://dx.doi.org/10.1177/2399808318763164
  3. E. Ho, "Smart subjects for a smart nation? Governing (smart) mentalities in Singapore," Urban Studies, vol. 54, no. 13, pp. 3101–3118, 2017. [Online]. Available:
    https://doi.org/10.1177/0042098016664305
  4. A. Meijer and M. P. R. Bolivar, "Governing the Smart city: A review of the literature on smart urban governance," International Review of Administrative Sciences, vol. 82, no. 2, pp. 392–408, 2016. [Online]. Available:
    https://doi.org/10.1177/0020852314564308
  5. A. Nambiar and D. Mundra, "An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management," Big Data and Cognitive Computing, vol. 6, no. 4, p. 132, 2022. https://doi.org/10.3390/bdcc6040132
  6. M. Anthony, P. Martins, F. Caldeira, and F. Sá, "An Evaluation of How Big-Data and Data Warehouses Improve Business Intelligence Decision Making," in Proc. Conference on Information Systems and Technologies, Cham: Springer, , pp. 609–619, 2020. 
    https://doi.org/10.1007/978-3-030-45688-7_61
  7. I. Megdiche, F. Ravat, and Y. Zhao, "A Use Case of Data Lake Metadata Management," in Data Lakes 2, , pp. 97–122, 2020.
    https://doi.org/10.1002/9781119720430.ch5
  8. M. A. Farnum et al., A Dimensional Warehouse for Integrating Operational Data from Clinical Trials, Database, 2019.
    https://doi.org/10.1093/database/baz039
  9. W. H. Inmon, “Building the Data Warehouse”, 4th ed., vol. 13, no. 401. 2005.
  10. E. Saddad, A. El-Bastawissy, O. Hegazy, and M. Hazman, "Towards an alternative Data Warehouses Architecture," in Proc. 14th International Conference on Hybrid Intelligent Systems (HIS 2014), Kuwait, , vol. 6, pp. 48–53, Dec. 14–16, 2014
  11. S. H. A. El-Sappagh, A. M. A. Hendawi, and A. H. El Bastawissy, "A proposed model for data warehouse ETL processes," Journal of King Saud University – Computer and Information Sciences, vol. 23, no. 2, pp. 91–104, 2011.
    https://doi.org/10.1016/j.jksuci.2011.05.005
  12. H. L. H. S. Warnars, L. S. Warnars, A. Ramadhan, T. Siswanto, and A. Doucet, "Data warehouse design for firefighters operational at the DKI Jakarta fire department," TEM Journal, vol. 13, no. 1, pp. 365–376, 2024.
    https://doi.org/10.18421/TEM131-38
  13. V. Belov, A. N. Kosenkov, and E. Nikulchev, "Experimental characteristics study of data storage formats for data marts development within data lakes," Applied Sciences (Switzerland), vol. 11, no. 18, p. 8651, 2021.
    https://doi.org/10.3390/app11188651
  14. K. Krishnan, Data Warehousing in the Age of Big Data, Elsevier Inc., 2013.
    https://doi.org/10.1016/B978-0-12-405891-0.00006-4
  15. R. G. Goss and K. Veeramuthu, "Heading towards big data: building a better data warehouse for more data, more speed, and more users," in Proc. ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference, 2013, pp. 220–225.
    https://doi.org/10.1109/ASMC.2013.6552808
  16. A. A. Harby and F. Zulkernine, "From Data Warehouse to Lakehouse: A Comparative Review," in Proc. 2022 IEEE International Conference on Big Data (Big Data), 2022. 
    https://doi.org/10.1109/BigData55660.2022.10020719
  17. A. Sebaa, F. Chikh, A. Nouicer, and A. Tari, "Research in Big Data Warehousing using Hadoop," J. Inf. Syst. Eng. Manag., vol. 2, no. 2, pp. 1–5, 2017.
    https://doi.org/10.1145/3090354.3090376
  18. D. Amo, P. Gómez, L. Hernández-Ibáñez, and D. Fonseca, "Educational Warehouse: Modular, Private and Secure Cloudable Architecture System for Educational Data Storage, Analysis and Access," Appl. Sci., vol. 11, p. 806, 2021.
    https://doi.org/10.3390/app11020806
  19. N. Gür, J. Nielsen, K. Hose, and T. B. Pedersen, "GeoSemOLAP: Geospatial OLAP on the Semantic Web made easy," in Proc. 26th Int. Conf. World Wide Web Companion, New York, NY, USA: ACM, pp. 213–217, 2017.
    https://doi.org/10.1145/3041021.3054731
  20. C. Thomsen and T. B. Pedersen, "pygrametl: A powerful programming framework for extract-transform-load programmers," in Proc. 12th ACM Int. Workshop Data Warehousing and OLAP,  pp. 49–56, 2009. 
    https://doi.org/10.1145/1651291.1651301