Modernizing ETL Architectures: A Survey of AI-Enhanced Real-Time Data Processing

Modern data processing systems have undergone significant transformation in response to exponential growth in data volume and processing requirements. This article presents a comprehensive survey of contemporary data processing architectures, examining the evolution from traditional batch processing to unified stream processing approaches. The article analyzes realworld implementations demonstrating substantial performance improvements, such as Apache Flink's achievement of processing 1.5 million events per second with 20ms latency, and cloud-based frameworks reducing processing time from 2 hours to 58 seconds for datasets of 8 million transactions. The article examines key architectural components including unified processing frameworks, cloudbased implementations, ETL systems, and IoT integration patterns. Through analysis of production deployments handling data volumes projected to reach 44 zettabytes by 2020, we identify critical challenges and solutions in data quality management, heterogeneous source integration supporting multiple database platforms including DB2, ORACLE, MSSQL, and MYSQL, and resource optimization. The findings indicate that modern architectures successfully address contemporary processing requirements while establishing foundations for future advancements in automated and intelligent processing capabilities.