In HBase, the RowKey column is used to uniquely identify each row in a table. It serves as the primary key and is crucial for efficient data retrieval in HBase tables.

In HiveQL, the EXPLAIN command is used to show the query execution plan. It provides insights into how Hive intends to execute the given query, including the sequence of tasks and operations involved. Analyzing the execution plan helps optimize queries for better performance.

Data Serialization in Hadoop is crucial for optimizing read/write operations on large datasets. Serialization is the process of converting complex data structures into a format that can be easily transmitted or stored. In Hadoop, this optimization helps in efficient data transfer and storage.

In Flume, the Channel Selector mechanism allows for dynamic data routing and transformation. It helps in directing incoming data to different channels based on specified criteria, enabling flexibility in data processing and handling.

The NameNode in Hadoop is responsible for metadata management, storing information about the location and health of data blocks. It doesn't store the actual data but keeps track of where data is stored across the cluster. This metadata is crucial for the proper functioning of the Hadoop Distributed File System (HDFS).

In a situation of slow data processing, prioritizing network optimization is crucial. This involves examining and enhancing the network infrastructure to reduce data transfer latency and improve overall cluster performance. Efficient data movement across nodes can significantly impact processing speed.

In Hadoop, 'Data Locality' is a common technique used for distributing data uniformly across the cluster. It aims to place computation close to the data, reducing data transfer overhead and improving overall performance.

In HDFS, the NameNode manages the file system namespace and regulates access to files. It keeps track of the metadata, such as file names and block locations, ensuring efficient file system operations.

Load balancing is a key factor in Hadoop development to ensure the scalability of applications. It involves distributing the computational workload evenly across the nodes in a cluster, preventing bottlenecks and optimizing resource utilization. This is crucial for maintaining performance as the system scales.

In MRUnit, Mocking is a crucial concept for validating the output of MapReduce jobs. It involves creating simulated objects (mocks) to imitate the behavior of real objects, allowing for effective testing of MapReduce programs without the need for a Hadoop cluster.