Apache Storm is a tool in Hadoop designed for efficient real-time stream processing. It allows for the processing of data in motion, making it suitable for scenarios where low-latency and real-time insights are crucial.

The primary role of Apache Sqoop in the Hadoop ecosystem is data ingestion. Sqoop facilitates the transfer of data between Hadoop and relational databases, making it easier to import and export structured data. It helps bridge the gap between the Hadoop Distributed File System (HDFS) and relational databases.

The primary benefit of using Avro in Hadoop ecosystems is high compression. Avro employs a compact binary format that results in efficient storage, reducing the amount of disk space required for storing data. This is especially crucial for handling large datasets in Hadoop environments.

Hadoop's HDFS High Availability feature employs a Standby NameNode to handle the failure of the primary NameNode. The Standby NameNode maintains a synchronized copy of the metadata, ready to take over in case the primary NameNode fails, ensuring continuous availability.

When setting up a new Hadoop cluster for massive data sets, one should consider Network Bandwidth as a key aspect. Efficient data loading and processing require a robust and high-speed network to facilitate seamless communication between nodes and ensure optimal data transfer rates.

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 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.

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.

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.

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.