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.

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.

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.

In scenarios requiring high throughput and low latency, configuring HBase for in-memory compaction can be beneficial. This involves keeping more data in memory, reducing the need for disk I/O and enhancing data access speed. It's particularly effective for read-heavy workloads with a focus on performance.

Hadoop uses Speculative Execution to ensure that data processing continues even if a node fails during a MapReduce job. The framework identifies slow-running tasks and launches backup tasks on other nodes, ensuring timely completion of the job.

Replication is a key feature of Hadoop that ensures data redundancy and fault tolerance. Hadoop replicates data blocks across multiple nodes in the cluster, reducing the risk of data loss in case of node failures and enhancing the system's overall reliability.