The technology used for real-time data processing includes stream processing frameworks, distributed computing, and in-memory databases. Stream processing frameworks, such as Apache Kafka and Apache Flink, allow for processing continuous streams of data in real time. Distributed computing enables parallel processing across multiple nodes, enhancing processing speed and efficiency. In-memory databases, like Redis and Memcached, store data in RAM instead of on disk, significantly accelerating access times. Together, these technologies empower organizations to analyze data as it arrives, enabling timely decisions and responses to ongoing events.