Low-code/no-code helps expand the data warehousing toolbox.
Keywords:
Low-code, No-code, Data Warehousing, Data IntegrationAbstract
Data process management has undergone significant changes in response to growing demand for fast data-driven decision-making and the requirement for adaptation and fast prototyping. Low-code/no-code (LCNC) systems have emerged as a powerful substitute offering a creative way to build, run, and maximize data pipelines and processes without calling for significant technical knowledge or coding ability. These platforms provide the business users, data analysts & the other stakeholders a straightforward, user-centric interface that lets them build complex data workflows, automate tasks & quickly draw insights free from the reliance on IT departments for every change. By integrating LCNC technologies into the data warehousing toolkits, companies may be speeding the deployment of data solutions, enhance communications between technical & non-technical teams & allow corporate users to take more active part in the data resource management. Particularly in improving productivity & reducing the time-to--market for data activities, the ability to simplify tasks such data integration, reporting & the analytics offers great benefits. Still, LCNC systems have some advantages but can create problems with scalability, data security, and governance.
References
1. Abouelyazid, M., & Xiang, C. (2019). Architectures for AI Integration in Next-Generation Cloud Infrastructure, Development, Security, and Management. International Journal of Information and Cybersecurity, 3(1), 1-19.
2. Dunie, R., Schulte, W. R., Cantara, M., & Kerremans, M. (2015). Magic Quadrant for intelligent business process management suites. Gartner Inc.
3. Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
4. Palmer, T. (2020). Microsoft PowerApps as an Alternative Solution to Business Application Development.
5. Saadeldin, R. (2019). of Thesis: The fundamental analysis of the software industry in the USA. change, 2019, 29.
6. Franzosa, R., & Hestermann, C. (2019). Magic quadrant for manufacturing execution systems. Gartner Inc., Stamford.
7. Petkova, M., Jekov, B., & Petkova, P. (2020, October). Administrative Automatic Solutions in Telecom Services. In 2020 28th National Conference with International Participation (TELECOM) (pp. 86-89). IEEE.
8. Jim, H. S., Hoogland, A. I., Brownstein, N. C., Barata, A., Dicker, A. P., Knoop, H., ... & Johnstone, P. A. (2020). Innovations in research and clinical care using patient‐generated health data. CA: a cancer journal for clinicians, 70(3), 182-199.
9. Soh, J., Singh, P., Soh, J., & Singh, P. (2020). Introduction to Azure machine learning. Data Science Solutions on Azure: Tools and Techniques Using Databricks and MLOps, 117-148.
10. Bernaschina, C. (2019). Tools, semantics and work-flows for web and mobile model driven development.
11. Khan, O. M. A., & Habib, K. (2020). Developing Multi-Platform Apps with Visual Studio Code: Get up and running with VS Code by building multi-platform, cloud-native, and microservices-based apps. Packt Publishing Ltd.
12. Sarsa, H. (2017). Critical Requirements of Internal Enterprise Mobile Applications (Master's thesis).
13. Fluri, B., Würsch, M., Giger, E., & Gall, H. C. (2009). Analyzing the co-evolution of comments and source code. Software Quality Journal, 17, 367-394.
14. Baldassarre, M. T., Barletta, V. S., Caivano, D., & Scalera, M. (2020). Integrating security and privacy in software development. Software Quality Journal, 28(3), 987-1018.
15. Holland, C. T. J., Tanenbaum, J., & CMUSEIPU States. (2020). Emerging technologies 2020: Six areas of opportunity. Software Engineering Institute.
16. Thumburu, S. K. R. (2020). Integrating SAP with EDI: Strategies and Insights. MZ Computing Journal, 1(1).
17. Thumburu, S. K. R. (2020). Exploring the Impact of JSON and XML on EDI Data Formats. Innovative Computer Sciences Journal, 6(1).
18. Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
19. Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
20. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
21. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
22. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
23. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
24. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
25. Thumburu, S. K. R. (2020). Interfacing Legacy Systems with Modern EDI Solutions: Strategies and Techniques. MZ Computing Journal, 1(1).