Book Title:
Mastering Data Integration and Analytics: Real-World Applications with PL/SQL, SQL, Informatica, Snowflake, and Python
Keywords:
SQL, Informatica, Snowflake, Python, Data Integration , AnalyticsSynopsis
Technological evolution, the process by which humanity has continuously refined, innovated, and expanded its technical knowledge and tools, stands as a testament to the boundless potential of human ingenuity. “From the earliest stone tools that marked the dawn of the Paleolithic era to the complex digital ecosystems that define the 21st century, technology has not only shaped but also been shaped by the societies it serves. The journey of technological progress began with rudimentary tools designed to solve immediate survival challenges, evolving over millennia into sophisticated systems that address increasingly complex needs and desires. Each phase of advancement brought about transformative shifts, from the Agricultural Revolution—where innovations like the plow and irrigation revolutionized food production—to the Industrial Revolution, which introduced mechanization and redefined the global economy and social structures. The advent of the Information Age, characterized by the rise of computers and the internet, further accelerated the pace of change, linking the world in unprecedented ways and democratizing access to knowledge. This ceaseless evolution of technology underscores the interplay between necessity, creativity, and discovery, as humans continue to push the boundaries of what is possible. It reflects a continuum where the past informs the present, and the present lays the groundwork for the future, ensuring that the drive for innovation remains an integral part of the human experience.
Chapters
-
Introduction to Technological Evolution
-
Chapter 1 Oracle PL/SQL
-
Chapter 2 Informatica PowerCenter
-
Chapter 3 Snowflake
-
Chapter 4 Python
-
Chapter 5 Oracle Data Integrator (ODI)
-
Chapter 6 Oracle Application Express (APEX)
-
Chapter 7 Workday Integration
-
Chapter 8 Workday Studio
-
Conclusion
References
L. Messeri and M. J. Crockett, “Artificial intelligence and illusions of understanding in scientific research,” Nature, vol. 627, no. 8002, pp. 49–58, 2024.
P. Giudici, M. Centurelli, and S. Turchetta, “Artificial Intelligence risk measurement,” Expert Syst. Appl., vol. 235, no. 121220, p. 121220, 2024.
M. Khaleel, Y. Nassar, and H. J. El-Khozondar, “Towards utilizing Artificial Intelligence in scientific writing,” Int. J. Electr. Eng. and Sustain., pp. 45–50, 2024.
A. Sinha, D. Sapra, D. Sinwar, V. Singh, and G. Raghuwanshi, “Assessing and mitigating bias in Artificial Intelligence: A review,” Recent Advances in Computer Science and Communications, vol. 17, no. 1, pp. 1–10, 2024.
M. Khaleel, A. A. Ahmed, and A. Alsharif, “Artificial Intelligence in Engineering,” Brilliance, vol. 3, no. 1, pp. 32–42, 2023.
S. Nyholm, “Artificial intelligence and human enhancement: Can AI technologies make us more (artificially) intelligent?,” Camb. Q. Healthc. Ethics, vol. 33, no. 1, pp. 76–88, 2024.
J. Shuford and M. M. Islam, “Exploring the latest trends in artificial intelligence technology: A comprehensive review,” Journal of Artificial Intelligence General science (JAIGS), vol. 2, no. 1, 2024.
M. Khaleel, “Intelligent Control Techniques for Microgrid Systems,” Brilliance, vol. 3, no. 1, pp. 56–67, 2023.
Z. Li, Z. A. Pardos, and C. Rena, “Aligning open educational resources to new taxonomies: How AI technologies can help and in which scenarios,” Comput. Educ., no. 105027, p. 105027, 2024.
M. Khaleel, S. A. Abulifa, and A. A. Abulifa, “Artificial intelligent techniques for identifying the cause of disturbances in the power grid,” Brilliance, vol. 3, no. 1, pp. 19–31, 2023.
B. Liu, L. Yu, C. Che, Q. Lin, H. Hu, and X. Zhao, “Integration and performance analysis of artificial intelligence and computer vision based on deep learning algorithms,” 2023.
N. R. Mannuru et al., “Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development,” Inf. Dev., 2023.
G. Rjoub et al., “A survey on explainable Artificial Intelligence for cybersecurity,” 2023.
M. Kandlhofer, G. Steinbauer, S. Hirschmugl-Gaisch, and P. Huber, “Artificial intelligence and computer science in education: From kindergarten to university,” in 2016 IEEE Frontiers in Education Conference (FIE), 2016.
E. Y. Zhang, A. D. Cheok, Z. Pan, J. Cai, and Y. Yan, “From Turing to transformers: A comprehensive review and tutorial on the evolution and applications of generative transformer models,” Sci, vol. 5, no. 4, p. 46, 2023.
Y. Cao, S. Tang, R. Yao, L. Chang, and X. Yin, “Interpretable hierarchical belief rule base expert system for complex system modeling,” Measurement (Lond.), vol. 226, no. 114033, p. 114033, 2024.
D. D. Cox and T. Dean, “Neural networks and neuroscience-inspired computer vision,” Curr. Biol., vol. 24, no. 18, pp. R921–R929, 2014.
S. Sharma and P. Chaudhary, “Chapter 4 Machine learning and deep learning,” in Quantum Computing and Artificial Intelligence, De Gruyter, 2023, pp. 71–84.
V. Galanos, “Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding.” The University of Edinburgh, 2023.
H. Hirsch-Kreinsen, “Artificial intelligence: a ‘promising technology,’” AI Soc., 2023.
S. Lins, K. D. Pandl, H. Teigeler, S. Thiebes, C. Bayer, and A. Sunyaev, “Artificial intelligence as a service: Classification and research directions,” Bus. Inf. Syst. Eng., vol. 63, no. 4, pp. 441–456, 2021.
B. Jena, S. Saxena, G. K. Nayak, L. Saba, N. Sharma, and J. S. Suri, “Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review,” Comput. Biol. Med., vol. 137, no. 104803, p. 104803, 2021.
R. Rojas, “The first code for computer chess,” in Konrad Zuse’s Early Computers, Cham: Springer Nature Switzerland, 2023, pp. 191–201.
B. Goertzel, “Artificial General Intelligence: Concept, state of the art, and future prospects,” J. Artif. Gen. Intell., vol. 5, no. 1, pp. 1–48, 2014.
A. M. Barrett and S. D. Baum, “A model of pathways to artificial superintelligence catastrophe for risk and decision analysis,” J. Exp. Theor. Artif. Intell., vol. 29, no. 2, pp. 397–414, 2017. Khaleel et al. IJEES Page | 20
J. Bell, “What is machine learning?,” Machine Learning and the City. Wiley, pp. 207–216, 21-May-2022.
Z. Ahmed et al., “Machine learning at Microsoft with ML.NET,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019.
D. Kreuzberger, N. Kühl, and S. Hirschl, “Machine learning operations (MLOps): Overview, definition, and architecture,” IEEE Access, vol. 11, pp. 31866–31879, 2023.
S. Secinaro, D. Calandra, A. Secinaro, V. Muthurangu, and P. Biancone, “The role of artificial intelligence in healthcare: a structured literature review,” BMC Med. Inform. Decis. Mak., vol. 21, no. 1, 2021.
Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, 2016.
C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, 2021.
I. H. Sarker, “Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions,” SN Comput. Sci., vol. 2, no. 6, 2021.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
D. Khurana, A. Koli, K. Khatter, and S. Singh, “Natural language processing: state of the art, current trends and challenges,” Multimed. Tools Appl., vol. 82, no. 3, pp. 3713–3744, 2023.
S. Chung, S. Moon, J. Kim, J. Kim, S. Lim, and S. Chi, “Comparing natural language processing (NLP) applications in construction and computer science using preferred reporting items for systematic reviews (PRISMA),” Autom. Constr., vol. 154, no. 105020, p. 105020, 2023.
Y. Kang, Z. Cai, C.-W. Tan, Q. Huang, and H. Liu, “Natural language processing (NLP) in management research: A literature review,” J. Manag. Anal., vol. 7, no. 2, pp. 139–172, 2020.
W. Phatthiyaphaibun et al., “PyThaiNLP: Thai natural language processing in Python,” arXiv [cs.CL], 2023.
M. Treviso et al., “Efficient methods for natural language processing: A survey,” Trans. Assoc. Comput. Linguist., vol. 11, pp. 826–860, 2023.
T. Ige, A. Kolade, and O. Kolade, “Enhancing border security and countering terrorism through computer vision: A field of artificial intelligence,” arXiv [cs.CV], 2023.
L. Kaiyue, L. Lei “Research and application of artificial intelligence in the field of vision system and network,” in 2019 5th International Conference on Advanced Computing, Networking and Security (ADCONS 2019), 2019.
Downloads
Published
6 May 2025
Series
Categories
Copyright (c) 2025 Shodh Sagar International Publications
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Details about this monograph
ISBN-13 (15)
978-93-49848-32-0
How to Cite
Somayajula, S. (2025). Mastering Data Integration and Analytics: Real-World Applications with PL/SQL, SQL, Informatica, Snowflake, and Python. Shodh Sagar International Publications. https://doi.org/10.36676/978-93-49848-32-0
