The post discusses the evolution from traditional batch-driven ETL processes to real-time streaming ETL using technologies like Apache Kafka, KSQL, and Kafka Connect. The author reflects on past experiences with batch processing and highlights the inherent latency issues it introduces. The narrative then shifts to a demonstration of streaming ETL using Apache Kafka, emphasizing how data can be processed in real-time using Kafka's Connect API, transformed with KSQL, and then further enriched and joined with other datasets. This transformation allows for immediate data availability for various applications, such as real-time customer notifications and analytics dashboards. The post details the technical steps taken to extract, transform, and load data from Oracle into Kafka and then to Elasticsearch, showcasing the ability to process and aggregate data in real-time. The author underscores the benefits of an event-driven architecture and the potential for more responsive and dynamic data-driven applications.