Mule provides batch processing capabilities that allow applications to split incoming messages into individual records, perform actions on each record in parallel, and report results. Batch processing is useful for integrating and synchronizing data between systems, extracting and loading large amounts of data into target systems, and handling large volumes of API data into legacy systems. It involves splitting messages into records, executing actions on each record in batches, and reporting outcomes.

1. Mule Batch Processing

2. Batch Processing Overview
• Mule possesses the ability to process messages in batches. Within an
application, you can initiate a batch job which is a block of code that
splits messages into individual records, performs actions upon each
record, then reports on the results and potentially pushes the
processed output to other systems or queues.

3. Benefits of Batch processing
Batch processing is particularly useful when working with the following
scenarios:
• Integrating data sets, small or large, streaming or not, to parallel process
records
• Synchronizing data sets between business applications, such as syncing
contacts between Netsuite and Salesforce, effecting "near real-time“ data
integration
• Extracting, transforming and loading (ETL) information into a target system
• Handling large quantities of incoming data from an API into a legacy system

4. Batch Processing Terminology
• Batch
• BatchCommit
• BatchJob
• BatchJobInstance
• BatchJobResult
• BatchMessageProcessor(Execute)
• BatchStep
• Record

5. Batch Processing Phases
• Batch processing in Mule takes place within four phases