ARCHIVES
Original Article
Architecting Cloud Data Warehouses for Personalized Investment and Wealth Management Analytics
Venkat Sunil Kumar Indurthy1
1 Software Developer, Compunnel Software Group Inc, USA
Published Online: January-April 2026
Pages: 624-631
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501074
References
1. P. Vassiliadis, “A survey of extract–transform–load technology,” Int. J. Data Warehousing Mining, vol. 5, no. 3, pp. 1–27, 2009.
2. P. Vassiliadis and A. Simitsis, “Extraction, transformation, and loading,” in Encyclopedia of Database Systems. New York, NY, USA: Springer, 2009, pp. 1095–1101.
3. D. Skoutas and A. Simitsis, “Designing ETL processes using semantic web technologies,” in Proc. 9th Int. ACM Workshop Data Warehousing and OLAP (DOLAP), USA, 2006, pp. 67–74.
4. S. N. Parul and S. Teggihalli, “Performance optimization for extraction, transformation, loading and reporting of data,” in Proc. IEEE Global Conf. Communication Technologies (GCCT), Thuckalay, India, 2015, pp. 516–519.
5. M. Hendayun, E. Yulianto, J. F. Rusdi, A. Setiawan, and B. Ilman, “Extract transform load process in banking reporting system,” MethodsX, vol. 8, Art. no. 101260, 2021.
6. G. G. W. Mhon and N. S. M. Kham, “ETL pre-processing with multiple data sources for academic data analysis,” in Proc. IEEE Conf. Computer Applications (ICCA), 2020, pp. 1–5.
7. K. C. Mondal, N. Biswas, and S. Saha, Role of Machine Learning in ETL Automation. Hershey, PA, USA: IGI Global, 2020.
8. J. C. Nwokeji and R. Matovu, “A systematic literature review on big data extraction, transformation and loading (ETL),” in Proc. Int. Conf. Intelligent Computing, vol. 2. Cham, Switzerland: Springer, 2021, pp. 308–324.
9. B. E. B. Semlali, C. El Amrani, and G. Ortiz, “SAT-ETL-Integrator: An extract-transform-load software for satellite big data ingestion,” J. Appl. Remote Sens., vol. 14, no. 1, Art. no. 018501, 2020.
10. J. Alwidian, S. A. Rahman, M. Gnaim, and F. Al-Taharwah, “Big data ingestion and preparation tools,” Modern Appl. Sci., vol. 14, no. 9, pp. 12–27, 2020.
11. M. Ghasemaghaei and G. Calic, “Can big data improve firm decision quality? The role of data quality and data diagnosticity,” Decis. Support Syst., vol. 120, pp. 38–49, 2019.
12. Y. Timmerman and A. Bronselaer, “Measuring data quality in information systems research,” Decis. Support Syst., vol. 126, Art. no. 113138, 2019.
13. Taleb, M. A. Serhani, and R. Dssouli, “Big data quality assessment model for unstructured data,” in Proc. 13th Int. Conf. Innovations in Information Technology (IIT), 2018, pp. 69–74.
14. C. Cichy and S. Rass, “An overview of data quality framework,” IEEE Access, vol. 7, pp. 24634–24648, 2019.
15. O. Azeroual, G. Saake, and M. Abuosba, “ETL best practices for data quality checks in RIS databases,” Informatics, vol. 6, no. 1, Art. no. 10, 2019.
16. S. Mathew, “Overview of Amazon Web Services,” 2017. [Online]. Available: https://aws.amazon.com. [Accessed: Apr. 6, 2019].
17. F. Kossmann, Z. Wu, E. Lai, N. Tatbul, L. Cao, T. Kraska, and S. Madden, “Extract-transform-load for video streams,” Proc. VLDB Endowment, vol. 16, no. 9, pp. 2302–2315, 2023.
18. R. M. Terol, A. R. Reina, S. Ziaei, and D. Gil, “A machine learning approach to reduce dimensional space in large datasets,” IEEE Access, vol. 8, pp. 148181–148192, 2020.
2. P. Vassiliadis and A. Simitsis, “Extraction, transformation, and loading,” in Encyclopedia of Database Systems. New York, NY, USA: Springer, 2009, pp. 1095–1101.
3. D. Skoutas and A. Simitsis, “Designing ETL processes using semantic web technologies,” in Proc. 9th Int. ACM Workshop Data Warehousing and OLAP (DOLAP), USA, 2006, pp. 67–74.
4. S. N. Parul and S. Teggihalli, “Performance optimization for extraction, transformation, loading and reporting of data,” in Proc. IEEE Global Conf. Communication Technologies (GCCT), Thuckalay, India, 2015, pp. 516–519.
5. M. Hendayun, E. Yulianto, J. F. Rusdi, A. Setiawan, and B. Ilman, “Extract transform load process in banking reporting system,” MethodsX, vol. 8, Art. no. 101260, 2021.
6. G. G. W. Mhon and N. S. M. Kham, “ETL pre-processing with multiple data sources for academic data analysis,” in Proc. IEEE Conf. Computer Applications (ICCA), 2020, pp. 1–5.
7. K. C. Mondal, N. Biswas, and S. Saha, Role of Machine Learning in ETL Automation. Hershey, PA, USA: IGI Global, 2020.
8. J. C. Nwokeji and R. Matovu, “A systematic literature review on big data extraction, transformation and loading (ETL),” in Proc. Int. Conf. Intelligent Computing, vol. 2. Cham, Switzerland: Springer, 2021, pp. 308–324.
9. B. E. B. Semlali, C. El Amrani, and G. Ortiz, “SAT-ETL-Integrator: An extract-transform-load software for satellite big data ingestion,” J. Appl. Remote Sens., vol. 14, no. 1, Art. no. 018501, 2020.
10. J. Alwidian, S. A. Rahman, M. Gnaim, and F. Al-Taharwah, “Big data ingestion and preparation tools,” Modern Appl. Sci., vol. 14, no. 9, pp. 12–27, 2020.
11. M. Ghasemaghaei and G. Calic, “Can big data improve firm decision quality? The role of data quality and data diagnosticity,” Decis. Support Syst., vol. 120, pp. 38–49, 2019.
12. Y. Timmerman and A. Bronselaer, “Measuring data quality in information systems research,” Decis. Support Syst., vol. 126, Art. no. 113138, 2019.
13. Taleb, M. A. Serhani, and R. Dssouli, “Big data quality assessment model for unstructured data,” in Proc. 13th Int. Conf. Innovations in Information Technology (IIT), 2018, pp. 69–74.
14. C. Cichy and S. Rass, “An overview of data quality framework,” IEEE Access, vol. 7, pp. 24634–24648, 2019.
15. O. Azeroual, G. Saake, and M. Abuosba, “ETL best practices for data quality checks in RIS databases,” Informatics, vol. 6, no. 1, Art. no. 10, 2019.
16. S. Mathew, “Overview of Amazon Web Services,” 2017. [Online]. Available: https://aws.amazon.com. [Accessed: Apr. 6, 2019].
17. F. Kossmann, Z. Wu, E. Lai, N. Tatbul, L. Cao, T. Kraska, and S. Madden, “Extract-transform-load for video streams,” Proc. VLDB Endowment, vol. 16, no. 9, pp. 2302–2315, 2023.
18. R. M. Terol, A. R. Reina, S. Ziaei, and D. Gil, “A machine learning approach to reduce dimensional space in large datasets,” IEEE Access, vol. 8, pp. 148181–148192, 2020.
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