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.

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.

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.

Apache Sqoop is significant in Hadoop data pipelines as it facilitates the import and export of data between Hadoop and relational databases. It streamlines the transfer of data, allowing seamless integration between Hadoop's distributed storage and traditional relational databases.

Apache Spark's high processing speed is attributed to its in-memory processing feature. Unlike traditional MapReduce, Spark stores intermediate data in memory, reducing the need for time-consuming disk I/O operations and accelerating data processing.

When tuning a Hadoop cluster, adjusting the YARN Container Size is essential for optimal use of cluster resources. Properly configuring the container size ensures efficient resource utilization and helps in avoiding resource contention among applications running on the cluster.

In HiveQL, the command used to load data into a Hive table is LOAD DATA. This command is used to copy data from an external table or a local file system into a Hive table, making the data accessible for querying and analysis.

Parquet's Compression optimization reduces storage requirements and minimizes I/O operations during data analysis. It improves performance by efficiently storing and retrieving data in a compressed format.

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.

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.

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.

The SequenceFile format in Hadoop is particularly suited for sequential data access. It is optimized for storing large amounts of data in a serialized, binary format, making it efficient for applications that require sequential read and write access, such as MapReduce tasks.