Integrating Kubernetes Autoscaling for Cost Efficiency in Cloud Services

This research paper explores the best practices for developing scalable Representational State Transfer (REST) APIs in cloud environments. As the demand for robust and high-performance APIs continues to grow, developers face numerous challenges in designing and implementing scalable solutions. This study examines various aspects of API development, including architectural principles, cloud-native technologies, performance optimization techniques, and security considerations. By synthesizing current research and industry practices, this paper provides a comprehensive guide for practitioners and researchers in the field of API development for cloud environments.

Best Practices for Designing Scalable REST APIs in Cloud Environments

Sachin Bhatt

2024

Security and Compliance Considerations for Running SAP Systems on AWS

Sachin Bhatt

2024

This research work focuses on Amazon Web Services’ security and compliance requirements while hosting SAP environments. It reviews aspects of security particularly on data, encryption, identity and access control and security of networks. Emerging compliance issues regarding GDPR, HIPAA, and SOX are explained to cover data location and control. The paper also outlines how best to approach security control management and how to use AWS tools to overcome these challenges. Identifying the critical measures to ensure compliance and security is made; these are strongly encouraged to be managed as well as applied actively and with the help of key features of AWS.

Optimizing Test Data Management Strategies in Banking Domain Projects

Ashwini Shivarudra

2024

The steady rise in processing power over the past 20 years has resulted in an enormous volume of data. Furthermore, anybody may now easily create and consume material in any format thanks to recent advancements in Web technology. Large volumes of data are regularly gathered by banking systems, including trade finance data, SWIFT and telex communications, client information, details about transactions, risk profiles, credit card details, limit and collateral details, the compliance or Anti Money Laundering (AML)-related data, and limit and collateral details. Every day, thousands of choices are made at banks. These choices pertain to credit, default, beginning a relationship, investments, AML, and illicit funding, among other things. To make these crucial choices, one must rely on a variety of data and drill down capabilities offered by the banking systems. We created a set of specifications for the kinds of data that should be included in a product catalogue. We ascertained what data the departments need by using a survey and questionnaire of the retailer's staff. We ensured that there was no one standard for the information organisation and then put out our own plan. This enormous amount of data may be mined for information and intriguing patterns, which can then be used to the decision-making process. This article examines and summarises a number of data mining methods that have applications in the banking industry. An overview of data mining methods and procedures is given. It also sheds light on how these methods may be applied in the banking industry to facilitate and enhance decision-making.