The primary tool for diagnosing HDFS corruption issues in Hadoop is FSCK (File System Check). FSCK checks the integrity of HDFS files and detects any corruption or inconsistencies, helping administrators identify and repair issues related to data integrity.

A Replication strategy is essential to handle node failures in a Hadoop cluster. HDFS uses replication to ensure fault tolerance by storing multiple copies (replicas) of data across different nodes in the cluster. This redundancy helps in recovering from node failures.

Apache Impala differs from Hive in terms of data processing by utilizing in-memory processing. Impala is designed for low-latency SQL queries on Hadoop data, and it processes data in-memory, providing faster query performance compared to traditional Hive queries.

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.

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.

--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 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.

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.

Apache Flume ensures data reliability during transfer to HDFS through an acknowledgment mechanism. This mechanism involves confirming the successful receipt of data events, ensuring that no data is lost during the transfer process. It contributes to the reliability and integrity of the data being ingested into Hadoop.

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.

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.

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.