In Hadoop, the Secondary NameNode is commonly used for creating consistent backups of data stored in HDFS. It performs periodic checkpoints of the namespace metadata, reducing the recovery time in case of a NameNode failure.

The ResourceManager in YARN is responsible for monitoring the resource usage in a node and managing the user's job execution. It keeps track of available resources and allocates them to applications.

A common issue in Hadoop clusters, where one node is overloaded while others are underutilized, is known as Load Balancing. Load balancing ensures that the computational workload is evenly distributed across all nodes, optimizing resource utilization and preventing performance bottlenecks.

Python is commonly used for scripting Hadoop jobs due to its simplicity and ease of use. It provides a high-level scripting interface, making it convenient for writing Hadoop jobs, especially for tasks like data processing and analysis.

In Hadoop development, the principle of Data Locality is essential for managing large-scale data processing. Data Locality ensures that data is processed on the same node where it is stored, reducing data transfer overhead and enhancing the efficiency of data processing in Hadoop.

A primary advantage of real-time processing over batch processing is low latency. Real-time processing systems aim to provide quick and immediate results, making them suitable for applications that require rapid data analysis and response times.

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.

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.

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.

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.

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 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.