AI/ML-Driven Predictive Analytics on SAP BW/4HANA Using AWS Athena and Delta Lake for Scalable Financial and Operational Intelligence
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I1P120Keywords:
SAP BW/4HANA, AWS Athena, Delta Lake, Predictive Analytics, SAP BW, Machine Learning, Financial Intelligence, Operational Intelligence, Data Lake, Cloud Computing, Big Data AnalyticsAbstract
The rapid data volume increase in any enterprise has resulted in the need to build data analytics frameworks that are scalable, efficient, and intelligent enough to help convert raw data into actionable insights. Existing organizations are becoming more dependent on sophisticated analytics solutions as a competitive edge, process optimization, and monetary decision-making. The paper will provide a full architecture of AI/ML-based predictive analytics with SAP BW/4HANA, using AWS Athena and Delta Lake to facilitate scale in financial and operational intelligence. SAP BW/4HANA is an effective enterprise data warehouse that is in-memory computing optimized and allows real-time analytics and data modeling speedy. Nevertheless, classic BW systems are challenged with the capabilities of processing unstructured information, processing at scale on distributed computers and machine learning applications. In order to overcome them, in this study, a hybrid architecture, which is a combination of SAP BW/4HANA and AWS-native cloud-native services (aws athena as the serverless querying engine and Delta Lake as trusted data lakes with ACID transactions), will be suggested. The suggested framework allows the easy data extraction of SAP BW/4HANA into a scaled data lake setting, in which Delta Lake is utilized to guarantee the integrity of data, enforce data schemas, and provide time travel. The AWS Athena supports a cost-effective, rapid querying of large data sets in the Amazon S3 storage, without the need to provide infrastructure. The combination of AI/ML models allows making financial forecasting, anomaly detection, and optimization of operation. The current study proposes a multi-layered architecture that includes the data ingestion, storage, processing and analytics as well as visualization layers. Regression, time-series forecasting, and classification algorithms based on machine learning are used to predict financial and operational data. To guarantee accuracy and scalability, the system also includes feature engineering, pipelines of data transformation and mechanisms of model evaluation. The procedure involves ETL/ELT operations, Delta lake schema design, query optimization in Athena and model training via distributed computing frameworks. The performance of the solutions is determined according to the scalability, query latency, accuracy of the predictions, and cost effectiveness. Findings show tremendous response time acceleration in query performance and information reliability in predictive accuracy in comparison to the conventional SAP only systems. Moreover, the article reveals the main issues of data regulation, the complexity of integration, and security concerns and suggests their solutions, including metadata managing, encryptions, and access controls. The results indicate that SAP BW/4HANA and AWS Athena and Delta Lake are an effective system of predictive analytics at an enterprise level. The paper will be relevant in the context of enterprise analytics by showing the possibility to build a scalable, cost-effective, and high-performance architecture that will allow connecting to both the traditional data warehousing and the current technologies of the data lake and machine learning. The suggested structure can be embraced in all the sectors to improve financial planning, risk management, and efficiency of operations, ensuring that intelligent and data-driven businesses emerge.
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