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.

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.

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.

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.

MRUnit tests can be written in Java to simulate the MapReduce environment. MRUnit is a testing framework for Apache Hadoop MapReduce jobs, allowing developers to write unit tests for their MapReduce programs.

The Shuffle function in Spark is critical for performing wide transformations like groupBy. It involves redistributing and exchanging data across the partitions, typically occurring during operations that require data to be grouped or aggregated across the cluster.

Mockito is a common Java mocking framework used in unit testing Hadoop applications. It enables developers to create mock objects for HDFS and MapReduce functionalities, allowing for isolated testing of individual components without relying on a full Hadoop cluster.

In Hadoop, the traditional framework used for batch processing is Apache Hadoop MapReduce. It is a programming model and processing engine that enables the processing of large datasets in parallel across a distributed cluster.

Ambari plays a critical role in managing and monitoring Hadoop clusters. It provides an intuitive web-based interface for administrators to configure, manage, and monitor Hadoop services, ensuring the health and performance of the entire cluster.

Apache Storm distinguishes itself in the Hadoop ecosystem by specializing in stream processing. It is designed to handle real-time data streaming and enables the processing of data as it arrives, making it suitable for applications that require low-latency and continuous data processing.