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.

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.

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.

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.

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.

When migrating large datasets to Hadoop, considerations for data integrity and efficiency should include ensuring data consistency and validation. It involves verifying that data is accurately transferred, maintaining its integrity during the migration process.

The primary tool used for monitoring Hadoop cluster performance is Hadoop ResourceManager. It provides information about the resource utilization, job execution, and overall health of the cluster. Administrators use ResourceManager to ensure efficient resource allocation and identify any performance bottlenecks.

Hive uses Predicate Pushdown to optimize query performance by pushing the filtering conditions closer to the data source, reducing the amount of data scanned during a query and improving overall efficiency.

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.

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.

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.

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.