What is the difference between batch processing and streaming processing in Spark?

Batch processing and streaming processing in Spark are two distinct modes of data processing.

1. Batch processing:

1. Batch processing is a static method of data processing where input data is divided into groups of batches for processing.
2. Batch processing is suitable for scenarios where static datasets are processed offline or data is processed in bulk at regular intervals.
3. Batch processing is suitable for handling large-scale datasets as it can process a large amount of data at fixed time intervals.
4. Batch processing typically follows a set data processing logic and does not gather the latest data in real time.

1. Stream processing:

1. Stream processing is a dynamic way of handling data, allowing data to be processed in real-time as it arrives, one piece at a time.
2. Stream processing is suitable for scenarios that require fast response and real-time data processing, such as real-time monitoring or real-time analysis.
3. Stream processing is event-driven and allows for dynamic adjustment of processing logic based on real-time data.
4. Data processing in streaming typically involves considering issues such as data timeliness and fault tolerance to ensure the accuracy and integrity of data processing.

In general, batch processing is suitable for offline processing of static data, while stream processing is suitable for real-time processing of dynamic data. In practical applications, the appropriate data processing mode can be chosen based on the specific requirements.