Apache Storm distinguishes itself in the Hadoop ecosystem by specializing in stream processing. It is designed to handle real-time data streaming and enables the processing of data as it arrives, making it suitable for applications that require low-latency and continuous data processing.

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.

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.

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.

In cases of uneven data distribution, the Partitioner in MapReduce helps balance the load by ensuring that data with the same key goes to the same reducer. This helps in achieving a more even distribution of processing tasks among reducers, improving performance.