Integrating Amazon EKS with CI/CD Pipelines for Efficient Application Delivery
Keywords:
Amazon EKS, Cloud-native applicationsAbstract
Integrating Amazon Elastic Kubernetes Service (EKS) with Continuous Integration and Continuous Deployment (CI/CD) pipelines is a powerful approach for streamlining application delivery processes. By leveraging EKS, businesses can manage containerized applications efficiently in a scalable, secure, and highly available environment. Integrating EKS with CI/CD tools enables automation of the entire software development lifecycle, from code commit to production deployment. This process minimizes human error, speeds delivery times, and ensures consistency across development, testing, and production environments. Developers can build, test, and deploy applications faster using CI/CD pipelines to automatically trigger builds and deploy containers to EKS clusters, providing a seamless flow from code to production. Additionally, this integration ensures that updates are consistently tested, validated, and deployed with minimal downtime, improving overall reliability and user experience. The flexibility of EKS allows teams to quickly scale resources based on demand, making it an ideal solution for applications of all sizes. By automating repetitive tasks and reducing manual intervention, companies can focus more on innovation and less on infrastructure management. This paper explores the best practices for integrating EKS with popular CI/CD tools like Jenkins, GitLab, and CircleCI, providing a roadmap for organizations looking to optimize their DevOps pipelines. Ultimately, this integration empowers development teams to deliver high-quality software rapidly and efficiently while maintaining the reliability and scalability needed for modern cloud-native applications.
Downloads
References
Bryant, D., & Marín-Pérez, A. (2018). Continuous delivery in java: essential tools and best practices for deploying code to production. O'Reilly Media.
Chen, G. (2019). Modernizing Applications with Containers in Public Cloud. Amazon Web Services.
Arundel, J., & Domingus, J. (2019). Cloud Native DevOps with Kubernetes: building, deploying, and scaling modern applications in the Cloud. O'Reilly Media.
Saito, H., Lee, H. C. C., & Wu, C. Y. (2019). DevOps with Kubernetes: accelerating software delivery with container orchestrators. Packt Publishing Ltd.
Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).
Labouardy, M. (2018). Hands-On Serverless Applications with Go: Build real-world, production-ready applications with AWS Lambda. Packt Publishing Ltd.
Farcic, V. (2019). The DevOps 2.4 Toolkit: Continuous Deployment to Kubernetes: Continuously Deploying Applications With Jenkins to a Kubernetes Cluster. Packt Publishing Ltd.
Amazon, E. C. (2015). Amazon web services. Available in: http://aws. amazon. com/es/ec2/(November 2012), 39.
WEB, E., DE PADRES, A. T. E. N. C. I. Ó. N., SOCIAL, S., & TAPIAS, M. J. J. (2009). Sobre nosotros. Línea) México, disponible en http://www. perotes-pedrugada. com/contacto. asp (accesado el 20 de Junio de 2009.➢ Wikipedia, La Enciclopedia Libre (2009)“Embutido”(En Línea) disponible en es. wikipedia. org/wiki/Embutido.
King, B. M., & Minium, E. W. (2003). Statistical reasoning in psychology and education. New York: Wiley.
Hyldegård, J. (2004). Det personlige informationssystem. Biblioteksarbejde, (69), 31-40.
Paakkunainen, O. (2019). Serverless computing and FaaS platform as a web application backend.
Mehtonen, V. (2019). Research on building containerized web backend applications from a point of view of a sample application for a medium sized business.
Sahin, M. (2019). GitOps basiertes Continuous Delivery für Serverless Anwendungen (Master's thesis).
Freeman, R. T. (2019). Building Serverless Microservices in Python: A complete guide to building, testing, and deploying microservices using serverless computing on AWS. Packt Publishing Ltd.
Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
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).
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).
Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
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
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
Naresh Dulam, et al. “Kubernetes Operators: Automating Database Management in Big Data Systems”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
Naresh Dulam. NoSQL Vs SQL: Which Database Type Is Right for Big Data?. Distributed Learning and Broad Applications in Scientific Research, vol. 1, May 2015, pp. 115-3
Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
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
Muneer Ahmed Salamkar, and Karthik Allam. Architecting Data Pipelines: Best Practices for Designing Resilient, Scalable, and Efficient Data Pipelines. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.
