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.

MRUnit tests can be written in Java to simulate the MapReduce environment. MRUnit is a testing framework for Apache Hadoop MapReduce jobs, allowing developers to write unit tests for their MapReduce programs.

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.

In a scenario with high concurrency and the need for near-real-time analytics, Presto would be recommended. Presto is designed for high-performance, distributed SQL queries, and it excels in scenarios with concurrent queries and the need for low-latency responses, making it suitable for real-time analytics.

In Hadoop, the Hadoop Audit Framework provides a framework for auditing and monitoring user accesses and activities. It logs relevant information, such as user actions and system events, facilitating security audits and compliance checks.