Apache HBase utilizes a column-family store as its primary storage model. Data is organized into column families, which consist of columns containing related data. This design allows for efficient storage and retrieval of large amounts of sparse data.

To manage Hadoop's file system namespace, a Hadoop administrator uses the ResourceManager. The ResourceManager is responsible for managing and scheduling resources across the Hadoop cluster, including handling job submissions and monitoring their execution.

In a Kerberized Hadoop cluster, the Ticket Granting Service (TGS) issues tickets for authenticated users. These tickets are then used to access various services within the cluster securely.

Apache Flume ensures end-to-end data delivery through an acknowledgment mechanism. It confirms the successful receipt of events, providing reliability in the face of network failures. This mechanism helps maintain data integrity and consistency throughout the data collection process.

The HDFS Storage Policies feature allows administrators to specify policies for moving and storing data blocks based on factors like performance, reliability, and cost. It provides flexibility in managing data storage within the Hadoop cluster.

Apache Oozie is primarily used for workflow orchestration in the Hadoop ecosystem. It allows users to define and manage workflows of Hadoop jobs, making it easier to coordinate and schedule complex data processing tasks in a distributed environment.

In advanced Hadoop data pipelines, Avro is used for efficient data serialization and storage. Avro is a binary serialization format that provides a compact and fast way to serialize data, making it suitable for Hadoop applications where efficiency is crucial.

The High Availability architecture in Hadoop is designed to avoid a single point of failure in the filesystem. It ensures that critical components like the NameNode have redundancy and failover mechanisms in place to maintain continuous operation even if a node fails.

Data Pruning is a technique in Hadoop used to optimize data transformation by processing only relevant data. It involves eliminating unnecessary data early in the processing pipeline, reducing the amount of data that needs to be processed and improving overall job performance.

Custom implementations in MapReduce often involve overriding the map() method for tailored data processing. The map() method defines how input data is transformed into intermediate key-value pairs, a crucial step in the MapReduce process.

The component of HDFS responsible for data replication and storage is DataNode. DataNodes are responsible for storing and managing the actual data blocks and replicating them to ensure fault tolerance.

Structuring a MapReduce job for optimal performance with diverse data types involves using appropriate Data Serialization techniques. This ensures efficient data transfer between Map and Reduce tasks, especially when dealing with varied data formats and structures.