Ambari plays a critical role in managing and monitoring Hadoop clusters. It provides an intuitive web-based interface for administrators to configure, manage, and monitor Hadoop services, ensuring the health and performance of the entire cluster.

In Hadoop, the traditional framework used for batch processing is Apache Hadoop MapReduce. It is a programming model and processing engine that enables the processing of large datasets in parallel across a distributed cluster.

Mockito is a common Java mocking framework used in unit testing Hadoop applications. It enables developers to create mock objects for HDFS and MapReduce functionalities, allowing for isolated testing of individual components without relying on a full Hadoop cluster.

The Shuffle function in Spark is critical for performing wide transformations like groupBy. It involves redistributing and exchanging data across the partitions, typically occurring during operations that require data to be grouped or aggregated across the cluster.

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.

In the case of a security breach, reviewing and tightening access controls is crucial. This involves restricting access privileges, ensuring least privilege principles, and regularly auditing and updating access permissions to minimize the risk of unauthorized access and data breaches.