In this episode, we delve into the world of Apache Flink, a powerful open-source system designed for both stream and batch data processing. We'll explore how Flink consolidates diverse data processing applications—including real-time analytics, continuous data pipelines, historical data processing, and iterative algorithms—into a single, fault-tolerant dataflow execution model.

Traditionally, stream processing and batch processing were treated as distinct application types, each requiring different programming models and execution systems. Flink challenges this paradigm by embracing data-stream processing as the unifying model. This approach allows Flink to handle real-time analysis, continuous streams, and batch processing with the same underlying mechanisms. We'll examine how this is achieved via durable message queues (like Apache Kafka or Amazon Kinesis), which enable Flink to process both the latest events in real-time, aggregate data in windows, or process historical data, depending on where in the stream the processing begins.

Key topics covered in this episode:

• Flink's Architecture


• Dataflow Graphs


• Stream Analytics


• Batch Processing


• Fault Tolerance


• Iterative Processing

References:

This episode draws primarily from the following paper:

• Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., & Tzoumas, K. (2015). Apache Flink: Stream and Batch Processing in a Single Engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 38(4).

The paper references several other important works in distributed data processing. Please refer to the full paper for a comprehensive list.

Disclaimer:

Please note that parts or all this episode was generated by AI. While the content is intended to be accurate and informative, it is recommended that you consult the original research papers for a comprehensive understanding.