What is the fundamental concept of Apache Beam?

The central concept of Apache Beam is to represent data processing tasks as data flow graphs, and provide a unified programming model for handling batch and stream processing tasks. Key concepts include:

1. Pipeline: an overall structure representing the data processing tasks, comprised of a series of data processing steps (Transforms).
2. PCollection (dataset): represents a collection of data in the dataflow graph, which can be an infinite stream of data (unbounded) or a finite batch of data (bounded).
3. Transformations: Operations used to manipulate and process data, such as map, filter, groupByKey, etc.
4. ParDo (Parallel Do): A transformation operation used to execute custom processing logic on a collection of data.
5. Source and Sink: Interfaces used for reading and writing data, able to integrate with various data storage systems.
6. Windowing: A mechanism used to divide and process infinite streaming data, supporting window operations based on time and other conditions.

By leveraging these core concepts, Apache Beam offers a highly flexible and scalable data processing framework that can accommodate various types of data processing requirements and enables a unified programming model across multiple data processing engines.