In this scenario, configuring Channel Multiplexing in Apache Flume would optimize data aggregation. This allows multiple channels to share the same source, efficiently aggregating data from various servers into the channels for processing.

Hadoop Streaming API's compatibility with Apache Pig is crucial in optimizing data processing, especially for handling large datasets. Pig allows developers to express data transformations using a high-level scripting language, making it easier to work with complex data processing tasks.

A Hadoop administrator can resolve a 'Data Skew' issue in a MapReduce job by using dynamic input splitting. This involves dynamically adjusting the input splits based on the size of the data to ensure that each mapper gets a balanced workload, mitigating the impact of data skew and improving overall job performance.

For disaster recovery, Hadoop clusters often use Cross-Datacenter replication. This involves replicating data across different geographical data centers, ensuring data availability and resilience in case of a disaster or data center failure.

In the context of Hadoop, YARN (Yet Another Resource Negotiator) plays a significant role in network capacity planning. YARN manages resources and schedules tasks across the cluster, optimizing the utilization of resources and enhancing network efficiency.

The choice of file format in Apache Hive plays a crucial role in optimizing query performance. ORC (Optimized Row Columnar) is specifically designed for high-performance analytics by organizing data in a way that minimizes I/O and improves compression, leading to faster query execution.

The replication factor in Hadoop impacts cluster capacity planning by improving fault tolerance. Higher replication ensures data availability even if some nodes fail. However, it comes at the cost of increased storage requirements. Capacity planning needs to balance fault tolerance with storage efficiency.

When dealing with intermittent failures, log analysis is the most effective monitoring approach for diagnosis. Examining Hadoop logs can provide insights into error messages, stack traces, and events that occurred during job execution, helping troubleshoot and identify the root cause of failures.

The crucial configuration for specifying the output key and value types in a MapReduce job is map.output.value.class. This configuration defines the data types emitted by the Mapper.

To enhance performance, Apache Spark is often configured in Hadoop clusters to manage large-scale data processing. Spark provides in-memory processing capabilities and a high-level API, making it suitable for iterative algorithms and interactive data analysis.

The primary purpose of Apache Pig in the Hadoop ecosystem is data analysis. It provides a platform for creating and executing data analysis programs using a high-level scripting language called Pig Latin, making it easier to work with large datasets.

Apache Pig's Nested Data feature enables the processing of nested data structures, providing flexibility in handling complex data types within the Hadoop ecosystem. It allows users to work with data that has varying and nested structures without predefined schemas.