Organizations are increasingly relying on ETL (Extract, Transform, Load) processes to manage the growing volume and velocity of data, with streaming ETL emerging as a powerful solution for real-time data processing. Unlike traditional batch ETL, which processes data in fixed intervals, streaming ETL operates continuously, allowing for real-time data ingestion, transformation, and delivery using frameworks like Apache Flink and Kafka. This approach is particularly beneficial for applications requiring immediate insights, such as fraud detection and IoT data monitoring, due to its low latency and scalability. However, streaming ETL introduces complexities such as maintaining data consistency and handling schema changes, which require careful implementation and use of modern stream processing platforms like DeltaStream. While both streaming and batch ETL have their advantages and drawbacks, the choice between the two depends on specific latency requirements, data volume, and processing needs. DeltaStream offers a platform to facilitate streaming ETL solutions by ensuring reliability and scalability, making it easier for organizations to adopt real-time data processing practices.