Apache Pig can be utilized for merging streaming and batch data by using Pig Streaming. This feature enables the integration of real-time data processing with batch processing in a seamless manner, making it suitable for scenarios that involve both streaming and batch data sources.

In a scenario of large-scale data processing, the key factor to consider for DataNode configuration is Network Bandwidth. Efficient data transfer between DataNodes is crucial to prevent bottlenecks and ensure timely processing of large volumes of data.

--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.

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.

For advanced data processing, MapReduce can be integrated with Apache Spark, a fast and general-purpose cluster computing system. Spark provides in-memory processing and higher-level APIs, making it suitable for complex data processing tasks.