Spring2024_Windowed_Aggregation_with_Apache_Flink

[RekhaSrinivas]

Enhance your streaming data analysis capabilities by developing a sophisticated windowed aggregation job using Apache Flink. Dive deeper into Flink's capabilities by exploring advanced windowing techniques like session windows or event-time processing to handle out-of-order data efficiently. Utilize Python to define intricate windowing logic, employing tumbling, sliding, or custom windows tailored to your specific use case. Extend the analysis by incorporating complex aggregation operations such as count, sum, average, or custom user-defined functions. Execute the Flink job within a notebook environment to visualize the aggregated results dynamically over time, empowering you to gain deeper insights into your streaming data.