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.

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.

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.

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.

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.

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.

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.