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.

Hive follows the Schema-on-Read approach, where the schema is applied when the data is read rather than when it is written. This flexibility is useful for handling diverse and evolving data in big data scenarios.

The primary goal of scaling a Hadoop cluster is to improve processing speed. Scaling allows the cluster to handle larger volumes of data and perform computations more efficiently by distributing the workload across a greater number of nodes. This enhances the overall performance of data processing tasks.

The primary role of a Hadoop Administrator is cluster management. They are responsible for the installation, configuration, and maintenance of Hadoop clusters. This includes monitoring the health of the cluster, managing resources, and ensuring optimal performance for data processing tasks.

The component in Apache Flume responsible for collecting data is the Source. Sources are responsible for ingesting data from various input points and forwarding it to the Flume agent for further processing and routing.

ZooKeeper plays a crucial role in managing a Hadoop cluster by providing centralized configuration management. It helps coordinate and synchronize distributed components, ensuring consistent and reliable configurations across the cluster, which is essential for the smooth operation of Hadoop services.

In the context of Big Data transformation, data integration is a key challenge when integrating diverse data sources in Hadoop. It involves harmonizing data from various sources, formats, and structures to create a unified and meaningful view for analysis.

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.