--network-throttle is a critical Sqoop configuration that helps balance network load and performance during data transfer. It allows users to control the rate at which data is transferred, optimizing the data transfer process.

In Flume, Interceptors are used for transforming incoming events before they are stored in the destination. They allow users to modify or augment the events as they flow through the Flume pipeline, providing flexibility in data processing.

In a high-throughput streaming data scenario, network bandwidth is essential in capacity planning. Streaming data applications rely on fast data movement, and sufficient network capacity ensures timely data transmission within the cluster.

Data Flow is a critical aspect in Hadoop cluster monitoring for understanding data processing patterns and anomalies. Monitoring data flow helps in identifying bottlenecks, optimizing performance, and ensuring efficient processing of large datasets.

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.

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.

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.

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.

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.

The concept of 'Schema on Read' is primarily associated with Apache Hive. In Hive, data is stored without a predefined schema, and the schema is applied at the time of reading/querying the data. This flexibility is beneficial for handling diverse data formats.