Hive uses SQL (Structured Query Language) for querying and managing large datasets. This allows users familiar with traditional relational database querying to work with big data stored in Hadoop without needing to learn complex programming languages like Java or MapReduce.

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.

Parquet uses a columnar storage format. Unlike row-based storage, where entire rows are stored together, Parquet organizes data column-wise. This approach enhances compression and facilitates more efficient query processing, making it suitable for analytics workloads.

Latency is a commonly used metric in Big Data analytics to measure the efficiency of data processing. It represents the time taken for data processing tasks, and lower latency is often desired for real-time or near-real-time analytics.

HDFS handles large files spanning multiple blocks through a technique called File Striping. It involves dividing a large file into fixed-size blocks and distributing these blocks across multiple nodes in the cluster. This striping technique allows for parallel data processing, enhancing performance.