To ensure minimal latency in data delivery for a real-time analytics application, configuring Flume to use a Memory Channel is crucial. Memory Channel stores events in memory, providing low-latency data transfer for real-time processing.

The Rack Awareness feature in Hadoop ensures that data blocks are stored on multiple racks, enhancing fault tolerance. This strategy reduces the risk of data loss in case an entire rack or network segment goes down, improving the overall reliability of the Hadoop cluster's data storage.

The primary benefit of using compression in Hadoop's MapReduce jobs is to reduce storage space. Compressing data before storing it allows for more efficient use of storage resources, enabling Hadoop clusters to handle and process larger volumes of data effectively. It doesn't directly impact data transfer speed or enhance data security but contributes to storage optimization.

In Hadoop, InputFormats are responsible for data serialization. InputFormats define how Hadoop should read and parse the input data from the underlying storage system. They specify how the data is to be interpreted, deserialized, and presented to the MapReduce job for further processing. Properly configured InputFormats are crucial for ensuring accurate data processing in Hadoop.

In a complex MapReduce job, the Partitioner plays a crucial role in data distribution. It determines how the key-value pairs outputted by the Map tasks are distributed to the Reducer tasks. An effective Partitioner ensures that similar keys end up in the same partition, optimizing data processing efficiency during the Reduce phase.

When dealing with data skew, using Combiners in a MapReduce job can help improve efficiency. Combiners perform local aggregation on the Mapper side, reducing the amount of data shuffled between Map and Reduce tasks and mitigating the impact of skewed data distribution.

Monitoring resource utilization metrics, such as CPU, memory, and disk usage, ensures optimal data throughput and processing efficiency in a high-traffic Hadoop environment. This strategy helps identify potential bottlenecks and allows for proactive optimization to maintain peak performance.

The primary role of Apache Oozie in the Hadoop ecosystem is workflow coordination. Oozie is a job scheduler that helps in managing and orchestrating workflows of Hadoop jobs, allowing users to define a series of tasks and their dependencies to execute complex data processing jobs.

Scalability is a key consideration in capacity planning for a rapidly expanding Hadoop environment. The architecture should be designed to scale horizontally, allowing the addition of nodes to accommodate growing data and processing needs seamlessly.

In optimizing MapReduce performance, the Shuffle phase plays a key role in managing memory and reducing disk I/O. It involves the exchange of data between the Map and Reduce tasks, and efficient shuffling contributes to overall job efficiency.