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.

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.

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.

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.

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.

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.

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.