Optimizing SaaS Metrics with Advanced Data Processing and Machine Learning Techniques

In SaaS applications, optimizing metrics is essential for enhancing efficiency, user satisfaction, and performance. This paper presents a framework integrating advanced data processing and intelligent algorithms within a scalable data lake architecture, leveraging AWS services like Kinesis for real-time data ingestion, Glue for ETL, S3 for storage, and Redshift for warehousing. Amazon SageMaker enables predictive analytics and real-time anomaly detection, with Athena and QuickSight supporting querying and visualization. Experimental results show improved data processing, anomaly detection, and predictive accuracy, contributing to efficient resource allocation and customer retention. This framework provides SaaS providers with a robust solution for achieving performance and scalability in multi-tenant environments.