Vol. 1 No. 2 (2021): African Journal of Artificial Intelligence and Sustainable Development
Articles

Adopting Microservices Architecture: Transformation, Benefits, and Challenges in Guidewire Applications

Ravi Teja Madhala
Senior Software Developer Analyst at Mercury Insurance Services, LLC, USA
Cover

Published 25-11-2021

Keywords

  • Microservices architecture,
  • Guidewire applications

How to Cite

[1]
Ravi Teja Madhala, “Adopting Microservices Architecture: Transformation, Benefits, and Challenges in Guidewire Applications ”, African J. of Artificial Int. and Sust. Dev., vol. 1, no. 2, pp. 482–507, Nov. 2021, Accessed: Dec. 28, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/237

Abstract

Microservices architecture has become a pivotal concept in modern software design, offering organizations greater flexibility, scalability, and improved integration across systems. This approach breaks down large, monolithic applications into more minor, independent services that can operate autonomously, making them easier to scale, maintain, and update. Adopting microservices can bring significant advantages for industries like insurance, where Guidewire applications play a crucial role in policy administration, claims management, & billing. Guidewire applications, traditionally built on a monolithic architecture, can significantly benefit from this shift, enabling insurers to respond faster to changing market demands, enhance system performance, and deliver a more personalized customer experience. The transformation from monolithic to microservices-driven applications empowers organizations to rapidly develop and deploy new features, reduce downtime, and optimize resource usage. Furthermore, microservices can improve operational efficiency by allowing teams to work on more minor, isolated services without the risk of affecting the entire system. Integrating new technologies and platforms also becomes more seamless, enhancing the overall value of the Guidewire ecosystem. However, this shift comes with challenges. Transitioning to a microservices-based system requires careful planning, a strong understanding of both the existing architecture and the target state, and effective management of the complexities involved in data consistency, service communication, and monitoring. Moreover, organizations must be prepared to invest in retraining teams, updating their infrastructure, & ensuring that the microservices are secure and properly managed. Despite these challenges, the potential for improved operational efficiency, faster time-to-market, and enhanced customer satisfaction makes adopting microservices an attractive option for Guidewire users aiming to future-proof their applications and stay ahead in a competitive market. This article explores the transformative power of microservices in Guidewire applications, shedding light on their key benefits, such as increased flexibility and scalability, while addressing the obstacles that organizations may face during implementation.

Downloads

Download data is not yet available.

References

  1. Hobert, K. A., Woodbridge, M., Mariano, J., & Tay, G. (2017). Magic quadrant for content services platforms. Gartner, Stamford, CT, available at: https://b2bsalescafe. files. wordpress. com/2017/11/magic-quadrant-for-content-services-platforms-oct-2017. pdf (accessed 15 October 2022).
  2. Team, P., & Campus, P. (2017). Placement Handout 2016-17. Placement Team, Pilani Campus.
  3. Woodbridge, M., Sillanpaa, M., & Severson, L. (2020). Magic Quadrant for Content Service Platforms.
  4. Kalske, M., Mäkitalo, N., & Mikkonen, T. (2018). Challenges when moving from monolith to microservice architecture. In Current Trends in Web Engineering: ICWE 2017 International Workshops, Liquid Multi-Device Software and EnWoT, practi-O-web, NLPIT, SoWeMine, Rome, Italy, June 5-8, 2017, Revised Selected Papers 17 (pp. 32-47). Springer International Publishing.
  5. Di Francesco, P., Lago, P., & Malavolta, I. (2018, April). Migrating towards microservice architectures: an industrial survey. In 2018 IEEE international conference on software architecture (ICSA) (pp. 29-2909). IEEE.
  6. De Lauretis, L. (2019, October). From monolithic architecture to microservices architecture. In 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (pp. 93-96). IEEE.
  7. Fritzsch, J., Bogner, J., Wagner, S., & Zimmermann, A. (2019, September). Microservices migration in industry: intentions, strategies, and challenges. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) (pp. 481-490). IEEE.
  8. Samad, A. (1924). Architectural Transition: Unveiling the Shift from Monolithic to Microservices in Digital Experience Platforms.
  9. Wolff, E. (2016). Microservices: flexible software architecture. Addison-Wesley Professional.
  10. Nadareishvili, I., Mitra, R., McLarty, M., & Amundsen, M. (2016). Microservice architecture: aligning principles, practices, and culture. " O'Reilly Media, Inc.".
  11. Balalaie, A., Heydarnoori, A., & Jamshidi, P. (2016). Migrating to cloud-native architectures using microservices: an experience report. In Advances in Service-Oriented and Cloud Computing: Workshops of ESOCC 2015, Taormina, Italy, September 15-17, 2015, Revised Selected Papers 4 (pp. 201-215). Springer International Publishing.
  12. Newman, S. (2019). Monolith to microservices: evolutionary patterns to transform your monolith. O'Reilly Media.
  13. Eski, S., & Buzluca, F. (2018, May). An automatic extraction approach: Transition to microservices architecture from monolithic application. In Proceedings of the 19th International Conference on Agile Software Development: Companion (pp. 1-6).
  14. Richards, M. (2015). Microservices vs. service-oriented architecture (pp. 22-24). Sebastopol: O'Reilly Media.
  15. Baber Khan, M. F. (2016). Spring Boot and Microservices: Accelerating Enterprise-Grade Application Development.
  16. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
  17. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
  18. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).
  19. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
  20. Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
  21. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
  22. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2).
  23. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
  24. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
  25. Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
  26. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
  27. Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
  28. Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ Computing Journal, 1(1).
  29. Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
  30. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
  31. Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
  32. Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
  33. Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
  34. Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
  35. Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
  36. Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
  37. Naresh Dulam, and Venkataramana Gosukonda. “AI in Healthcare: Big Data and Machine Learning Applications ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Aug. 2019
  38. Naresh Dulam. “Real-Time Machine Learning: How Streaming Platforms Power AI Models ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
  39. Naresh Dulam, et al. “Data As a Product: How Data Mesh Is Decentralizing Data Architectures”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
  40. Naresh Dulam, et al. “Data Mesh in Practice: How Organizations Are Decentralizing Data Ownership ”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
  41. Thumburu, S. K. R. (2020). Exploring the Impact of JSON and XML on EDI Data Formats. Innovative Computer Sciences Journal, 6(1).
  42. Thumburu, S. K. R. (2020). Large Scale Migrations: Lessons Learned from EDI Projects. Journal of Innovative Technologies, 3(1).
  43. Thumburu, S. K. R. (2020). Enhancing Data Compliance in EDI Transactions. Innovative Computer Sciences Journal, 6(1).
  44. Thumburu, S. K. R. (2020). Leveraging APIs in EDI Migration Projects. MZ Computing Journal, 1(1).
  45. Thumburu, S. K. R. (2020). A Comparative Analysis of ETL Tools for Large-Scale EDI Data Integration. Journal of Innovative Technologies, 3(1).
  46. Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
  47. Sarbaree Mishra. A Novel Weight Normalization Technique to Improve Generative Adversarial Network Training. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
  48. Sarbaree Mishra. “Moving Data Warehousing and Analytics to the Cloud to Improve Scalability, Performance and Cost-Efficiency”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
  49. Sarbaree Mishra, et al. “Training AI Models on Sensitive Data - the Federated Learning Approach”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
  50. Sarbaree Mishra. “Automating the Data Integration and ETL Pipelines through Machine Learning to Handle Massive Datasets in the Enterprise”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
  51. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
  52. Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
  53. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
  54. Komandla, Vineela. "Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction." Available at SSRN 4983012 (2018).