Hadoop MapReduce is a programming model and processing framework for processing large amounts of data in parallel across a distributed cluster. To prepare data for Hadoop MapReduce, you typically need to subdivide the data into smaller chunks or partitions that can be processed in parallel across multiple nodes in the cluster. Here’s how you can approach this:

1. Data Splitting: Divide your input data into smaller chunks or blocks. Hadoop’s HDFS (Hadoop Distributed File System) typically does this automatically when you upload your data, splitting it into 128MB or 256MB blocks. This is important for distributing the data efficiently across the cluster.
2. Mapper Tasks: In the context of MapReduce, each chunk of data is processed by a separate mapper task. Design your mapper function to process a single piece or record simultaneously. This function is applied to each data block independently and in parallel.
3. Key-Value Pairing: MapReduce processes data as key-value pairs. Your mapper function should read each record from the input data and emit intermediate key-value pairs. These intermediate key-value pairs are then grouped by key and passed to the reducer tasks.
4. Partitioning and Shuffling: Hadoop groups and shuffles the intermediate key-value pairs based on their keys. The shuffling phase ensures that all values for a given key are sent to the same reducer, allowing you to process the grouped data efficiently.
5. Reducer Tasks: Reducer tasks process the grouped and shuffled data. Your reducer function should take a key and a list of values associated with that key, and then perform the required aggregation or computation. Like mappers, reducers work in parallel.
6. Output: The output of reducer tasks is typically written to HDFS or another storage system. Ensure that your output format is compatible with your data processing needs.

Remember that the performance of your MapReduce job heavily depends on how well you distribute and partition your data. While this advice is specific to data processing in Hadoop MapReduce, it’s crucial to address any concerns about sending emails in bulk without triggering spam filters as well. Email deliverability involves factors like sender reputation, content quality, avoiding spammy tactics, and following email provider guidelines.