Platform as a Product: The Rise of Internal Developer Platforms (IDPs)
DOI:
https://doi.org/10.53555/ephijse.v7i4.265Keywords:
Internal Developer Platforms, Platform Engineering, DevOps, Developer Experience, Cloud Cost OptimizationAbstract
Internal Developer Platforms (IDPs) are revolutionizing the way companies create, deploy, and track applications, thereby redefining the perspective of platforms from just infrastructure to one of a tool. Often referred to as "Platform as a Product," this change underlines the acceleration of deployment speed, operational efficiency optimization, and enhancement of developer experience. Providing built-in security best practices, automation, and self-service tools, IDPs free development teams to concentrate more on code production than on maintenance. Those companies which use this approach have reduced cognitive load for engineers, faster time to market, and improved application reliability. Still, building and maintaining an IDP has other challenges: determining the appropriate degree of abstraction, guaranteeing governance while still allowing for flexibility, and encouraging team adoption among other things. This paper looks at the evolution of IDPs, basic concepts of platform-as---a-product thinking, and real-world case studies from companies that have successfully embraced these systems. Using technologies such Kubernetes, CI/CD pipelines & the policy-as-code frameworks, it investigates best ways to produce an IDP that balances the developer freedom with corporate standards. Internal Developer Platforms (IDPs) will be crucial as businesses extend their cloud-native architectures in maximizing operations, reducing cloud costs & encouraging innovation.
References
Immaneni, J. "Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success." Innovative Computer Sciences Journal 6.1 (2020).
Immaneni, Jayaram. "Using Swarm Intelligence and Graph Databases Together for Advanced Fraud Detection." Journal of Big Data and Smart Systems 1.1 (2020).
Sarbaree Mishra. A Distributed Training Approach to Scale Deep Learning to Massive Datasets. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
Sarbaree Mishra. Distributed Data Warehouses - An Alternative Approach to Highly Performant Data Warehouses. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019
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
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
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
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
Sairamesh Konidala. “What Is a Modern Data Pipeline and Why Is It Important?”. Distributed Learning and Broad Applications in Scientific Research, vol. 2, Dec. 2016, pp. 95-111
Sairamesh Konidala, et al. “The Impact of the Millennial Consumer Base on Online Payments ”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, June 2017, pp. 154-71
Sairamesh Konidala. “What Are the Key Concepts, Design Principles of Data Pipelines and Best Practices of Data Orchestration”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Jan. 2017, pp. 136-53
Sairamesh Konidala, et al. “Optimizing Payments for Recurring Merchants ”. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Aug. 2018, pp. 295-11
Sairamesh Konidala, et al. “A Data Pipeline for Predictive Maintenance in an IoT-Enabled Smart Product: Design and Implementation”. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Mar. 2018, pp. 278-94
Sairamesh Konidala. “Ways to Fight Online Payment Fraud”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Oct. 2019, pp. 1604-22
Sairamesh Konidala. “Cloud-Based Data Pipelines: Design, Implementation and Example”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019, pp. 1586-03
Sairamesh Konidala, and Jeevan Manda. “How to Implement a Zero Trust Architecture for Your Organization Using IAM”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Jan. 2020, pp. 1083-02
Sairamesh Konidala, et al. “Data Lakes Vs. Data Warehouses in Modern Cloud Architectures: Choosing the Right Solution for Your Data Pipelines”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020, pp. 1045-64
Nookala, G., et al. "End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges." Innovative Computer Sciences Journal 5.1 (2019).
Nookala, Guruprasad, et al. "Automating ETL Processes in Modern Cloud Data Warehouses Using AI." MZ Computing Journal 1.2 (2020).
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
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).
