Which statement about Batch Processing in Mule 4 is true?

• A. It is deprecated in Mule 4
• B. It requires an external scheduler
• C. It processes data in chunks but does not support On Complete
• D. It uses Batch Job, Batch Steps, and On Complete to process large data sets in chunks

Answer: (D)

Batch processing in Mule 4 is built to handle large data sets by dividing them into manageable chunks and processing those chunks, often in parallel to improve throughput. A Batch Job is the container that defines the overall work for the whole dataset. Inside this job, you configure one or more Batch Steps, each describing the logic to apply to each chunk of data. Mule automatically chunks the input, processes each chunk according to the steps you defined, and can run those steps in parallel to optimize performance. After every chunk has been processed, the On Complete phase runs to perform final tasks such as cleanup, aggregation, or reporting across the entire batch.

This approach does not require an external scheduler; you can trigger a batch from a flow or schedule it, but the batch engine itself handles the chunking and lifecycle. Therefore, using Batch Job, Batch Steps, and On Complete to process large data sets in chunks accurately describes how Mule 4 batch processing works.