The primary role of the Mapper in the MapReduce framework is data transformation. Mappers take input data and convert it into key-value pairs, which are then processed by the subsequent stages of the MapReduce job. This phase is crucial for dividing the workload and preparing the data for further analysis.

YARN stands for "Yet Another Resource Negotiator." It is the resource management layer in Hadoop that manages and negotiates resources for applications running on the Hadoop cluster. YARN separates the resource management functionality from MapReduce, making the Hadoop ecosystem more flexible and scalable.

The integration of Prometheus with Hadoop enhances real-time monitoring capabilities. Prometheus is a powerful open-source monitoring and alerting toolkit that provides robust support for collecting and querying metrics, enabling administrators to gain insights into the cluster's real-time performance and health.

The NameNode in Hadoop's architecture is responsible for managing metadata, such as the structure of the file system, permissions, and the mapping of data blocks to DataNodes.

Hadoop 2.x introduced the Standby NameNode to ensure high availability in the Hadoop cluster. The Standby NameNode maintains a copy of the metadata, and in case of a failure of the active NameNode, it can take over to avoid downtime and ensure continuous operation.

In Hadoop Streaming, the communication between the mapper and reducer is typically done through Key-Value pairs. The output of the mapper is sorted and grouped by keys before being passed to the reducer, facilitating the processing of data based on key associations.

The function of a Combiner in MapReduce is result aggregation. It combines (or aggregates) the intermediate output generated by the Mapper before sending it to the Reducer. This helps in reducing the volume of data transferred over the network and improves overall processing efficiency.

Hadoop's HDFS snapshots play a crucial role in point-in-time recovery. They capture the state of the file system at a specific point, allowing users to revert to that state in case of data corruption or accidental deletion. This feature enhances data recovery capabilities in Hadoop.

Hadoop's High Availability feature allows automatic failover of the NameNode service in case of a crash. This ensures continuous operation of the Hadoop cluster even in the face of a NameNode failure, enhancing the reliability of the system.

The operation used for joining two datasets in Apache Pig is the JOIN operation. It enables the combination of records from two or more datasets based on a specified condition, facilitating the merging of related information from different sources.