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.

In scenarios involving large-scale data aggregation, Apache HBase would be a suitable tool. HBase is a NoSQL database that provides real-time read and write access to large datasets, making it effective for quick data retrieval in aggregation scenarios.

Sqoop's incremental import feature benefits data ingestion in Hadoop by avoiding data duplication. It allows for importing only the new or modified data since the last import, reducing the amount of data transferred and optimizing the ingestion process.

Tuning YARN resource allocation parameters impacts the performance of a Hadoop cluster by optimizing resource utilization. Proper allocation ensures efficient task execution, maximizes parallelism, and minimizes resource contention, leading to improved overall cluster performance.

Hive's HQL (Hive Query Language) feature allows for the execution of MapReduce jobs with SQL-like queries. It provides a higher-level abstraction for processing data stored in Hadoop Distributed File System (HDFS) using familiar SQL syntax.