Apache Hive is primarily used for building data pipelines involving SQL-like queries in the Hadoop ecosystem. It provides a high-level query language, HiveQL, that allows users to express queries in a SQL-like syntax, making it easier for SQL users to work with Hadoop data.

Snapshot recovery techniques in Hadoop allow for the restoration of data to a specific point in time. Snapshots capture the state of the HDFS at a particular moment, providing a reliable way to recover data to a known and consistent state.

Partitioning data in HDFS improves processing efficiency by enhancing data locality. This means that computation is performed on nodes where the data is already stored, reducing the need for extensive data movement across the network and thereby improving overall processing speed.

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.

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 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 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.

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.

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.

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.

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.

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.