Apache Flink, an open-source distributed stream processing framework, has become a cornerstone in real-time data processing since its release in 2014, continually evolving to meet modern data processing demands. Key advancements include the introduction of stateful stream processing, event-time processing with watermarks, and the integration of the Blink Planner, enhancing SQL capabilities for batch and streaming data. Flink's native Kubernetes deployment supports cloud-native environments, simplifying cluster management, while improvements in checkpoints and savepoints enhance fault tolerance. PyFlink, Flink's Python API, and the introduction of Flink Stateful Functions have broadened its appeal, enabling integration with machine learning and supporting microservices architectures. Flink’s robust features and integrations have positioned it as a leader in real-time analytics, ensuring its relevance as organizations increasingly adopt real-time data-driven decision-making.