What is the main difference between Batch Processing and Stream Processing in Databricks?

The main difference between Batch Processing and Stream Processing in Databricks is that Batch Processing processes larger datasets at once, while Stream Processing processes data in real-time.

Batch Processing involves the collection and processing of a significant volume of data over a period of time. This approach is suitable for scenarios where it is acceptable to wait for all the data to accumulate before processing it. The results of batch processing are typically obtained after the entire dataset is processed, which might take some time depending on the size of the data.

On the other hand, Stream Processing is designed to handle data in real-time, allowing for the continuous processing of data as it becomes available. This method is crucial for applications that require immediate insights or actions based on incoming data, such as monitoring systems or real-time analytics.

Understanding the capability and use cases for both processing types is essential for choosing the right approach based on the requirements of your data workload.