What are the application scenarios of Flink in Java?

1. Stream data processing: Flink is a stream data processing engine that can be used to handle real-time data streams, such as real-time log analysis, real-time recommendation systems, and more.
2. Batch processing: Flink also supports batch processing mode, which can be used for handling large-scale batch data, such as data cleaning, ETL, etc.
3. Graph processing: Flink offers a graph processing library that can be used for analyzing graph data, such as social network analysis and path planning.
4. Flink offers a machine learning library that can be used for building and training machine learning models such as user behavior prediction and image recognition.
5. Complex event processing: Flink supports the processing of complex events, which can be used for monitoring and handling complex event streams, such as financial transaction monitoring, anomaly detection, and more.
6. Real-time data dashboard: Flink can be used to create real-time data dashboards for displaying live data analysis results.
7. Real-time alert system: Flink can be used to build real-time alert systems that promptly detect and address abnormal situations.

In short, Flink can be used in various real-time data processing and analysis scenarios, meeting various business needs.