In scenarios requiring large-scale data transformation, Apache Spark is often chosen for optimal performance. Spark's in-memory processing and efficient data processing engine make it suitable for handling complex transformations on large datasets with speed and scalability.

The Hadoop cluster configuration file that must be appropriately set up for efficient data processing is core-site.xml. This file contains configurations for the Hadoop core components and settings such as I/O settings and default filesystem name.

Ticket Granting Ticket (TGT) is a mechanism in Hadoop security that allows users to obtain and renew tokens for accessing various cluster services. It plays a crucial role in the Kerberos authentication process.

In the context of dealing with skewed data in MapReduce, Speculative Execution is a technique that helps distribute the load more evenly across reducers. It involves launching backup tasks for slow-running tasks on different nodes to ensure timely completion.

Configuring HDFS for high availability involves considering the Quorum Journal Manager, which ensures consistent metadata updates. It replaces the Secondary NameNode in maintaining the edit logs, enhancing fault tolerance and reliability in a high-availability setup.

Apache Spark is well-suited for real-time data processing in the Hadoop ecosystem. It offers in-memory processing and supports iterative algorithms, making it faster than traditional batch processing with MapReduce. Spark is particularly advantageous for applications requiring low-latency data analysis.

In Hadoop's MapReduce, the Shuffle phase occurs between the Map and Reduce phases. During this phase, the output from the Map phase is shuffled and sorted before being sent to the Reduce tasks for further processing.

In situations where data consistency is critical, the Incremental Import functionality of Sqoop should be emphasized. It allows for the extraction of only the new or updated data since the last import, ensuring consistency between Hadoop and the RDBMS.

The feature in Apache Flume that allows for the dynamic addition of new data sources during runtime is 'Source Interceptors.' These interceptors can be configured to modify, filter, or enrich events as they enter the Flume pipeline, facilitating the seamless integration of new data sources without interrupting the data flow.

High Availability (HA) in Hadoop ensures continued cluster operation in the event of the primary NameNode failure. With HA, a standby NameNode takes over seamlessly, preventing downtime and data loss.