Data, Analytics and Reporting solutions using AWS services for SAP workloads

Sachin Bhatt¹ & Prof. (Dr.) Mandeep Kumar²

¹Rajiv Gandhi Proudyogiki Vishwavidyalaya

Madhya Pradesh, India

² Sharda University

Greater Noida, India

Abstract– The convergence of Amazon Web Services (AWS) with SAP workloads for data analysis, reporting, and performance optimization has gained more attention between 2015 and 2020. As more and more companies are moving their enterprise resource planning (ERP) infrastructure to the cloud, AWS has become a popular platform due to its scalability, cost-effectiveness, and capability to handle complex analytics requirements. Studies point towards the transformation of SAP workload on AWS, from data processing in real time by SAP HANA to creating data lakes for big analytics with services like Amazon S3 and AWS Glue. Furthermore, machine learning capabilities of AWS, with offerings like SageMaker and Lambda, have allowed organizations to carry out predictive analytics on SAP data, thus enhancing decision-making processes in sectors. Despite the widespread use of AWS with SAP, there are still aspects of unawareness regarding the complexity of migration, integration of legacy systems, and consistency of performance across SAP applications. Current research indicates that organizations are benefiting from cost savings, enhanced reporting, and security compliance, but there are still challenges in re-engineering legacy systems, handling complexities of data integration, and achieving optimal cloud-native configurations. Additionally, there is an urgent need for more specialized research into the intersection of SAP’s next-generation analytical capabilities and AWS’s machine learning and artificial intelligence capabilities. This paper attempts to explore these research areas by offering a comprehensive review of existing research while also sharing insights on best practices, challenges, and future directions in integrating AWS services with SAP workloads to optimize data analytics, reporting, and performance.

Keywords — AWS, SAP workloads, cloud migration, data analytics, real-time reporting, machine learning, SAP HANA, data lakes, cost optimization, performance optimization, cloud-native integration, predictive analytics, security compliance, legacy system integration, business intelligence.

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