Hive integrates with Oozie for workflow scheduling in large-scale data warehousing environments. Oozie is a workflow scheduler system that allows users to define and manage Hadoop jobs, providing coordination and management of complex data processing tasks.

The process of commissioning or decommissioning nodes in a Hadoop cluster involves adding or removing data nodes. This dynamic adjustment helps in optimizing the cluster's capacity and resource utilization.

Kafka's Hash-based partitioning mechanism ensures that data with the same key is sent to the same partition, ensuring order and consistency in the distributed system. This is crucial for scalable and reliable data ingestion in Hadoop using Kafka.

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.

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.

Avro is an ideal choice when schema evolution is frequent and critical. Its schema is stored along with the data, allowing for flexible changes over time without requiring all consumers to be updated simultaneously.