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.

When dealing with time-series data in Hadoop, the optimal combination for performance is using the Parquet file format with Snappy compression. Parquet is columnar storage, and Snappy provides fast compression, making it efficient for analytical queries on time-series data.

For optimal results in aggregating data from multiple sources with Hadoop Streaming API, the approach should involve using Combiners for Intermediate Aggregation. Combiners help reduce the amount of data transferred between mappers and reducers, improving overall performance in the aggregation 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.

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.

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.

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.

Sqoop can be integrated with Shell scripts for custom data handling during import/export processes. This allows users to execute custom logic or transformations on the data as it is moved between Hadoop and relational databases.